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  • Data Quality and Master Data Management Resources

    - by Dejan Sarka
    Many companies or organizations do regular data cleansing. When you cleanse the data, the data quality goes up to some higher level. The data quality level is determined by the amount of work invested in the cleansing. As time passes, the data quality deteriorates, and you need to repeat the cleansing process. If you spend an equal amount of effort as you did with the previous cleansing, you can expect the same level of data quality as you had after the previous cleansing. And then the data quality deteriorates over time again, and the cleansing process starts over and over again. The idea of Data Quality Services is to mitigate the cleansing process. While the amount of time you need to spend on cleansing decreases, you will achieve higher and higher levels of data quality. While cleansing, you learn what types of errors to expect, discover error patterns, find domains of correct values, etc. You don’t throw away this knowledge. You store it and use it to find and correct the same issues automatically during your next cleansing process. The following figure shows this graphically. The idea of master data management, which you can perform with Master Data Services (MDS), is to prevent data quality from deteriorating. Once you reach a particular quality level, the MDS application—together with the defined policies, people, and master data management processes—allow you to maintain this level permanently. This idea is shown in the following picture. OK, now you know what DQS and MDS are about. You can imagine the importance on maintaining the data quality. Here are some resources that help you preparing and executing the data quality (DQ) and master data management (MDM) activities. Books Dejan Sarka and Davide Mauri: Data Quality and Master Data Management with Microsoft SQL Server 2008 R2 – a general introduction to MDM, MDS, and data profiling. Matching explained in depth. Dejan Sarka, Matija Lah and Grega Jerkic: MCTS Self-Paced Training Kit (Exam 70-463): Building Data Warehouses with Microsoft SQL Server 2012 – I wrote quite a few chapters about DQ and MDM, and introduced also SQL Server 2012 DQS. Thomas Redman: Data Quality: The Field Guide – you should start with this book. Thomas Redman is the father of DQ and MDM. Tyler Graham: Microsoft SQL Server 2012 Master Data Services – MDS in depth from a product team mate. Arkady Maydanchik: Data Quality Assessment – data profiling in depth. Tamraparni Dasu, Theodore Johnson: Exploratory Data Mining and Data Cleaning – advanced data profiling with data mining. Forthcoming presentations I am presenting a DQS and MDM seminar at PASS SQL Rally Amsterdam 2013: Wednesday, November 6th, 2013: Enterprise Information Management with SQL Server 2012 – a good kick start to your first DQ and / or MDM project. Courses Data Quality and Master Data Management with SQL Server 2012 – I wrote a 2-day course for SolidQ. If you are interested in this course, which I could also deliver in a shorter seminar way, you can contact your closes SolidQ subsidiary, or, of course, me directly on addresses [email protected] or [email protected]. This course could also complement the existing courseware portfolio of training providers, which are welcome to contact me as well. Start improving the quality of your data now!

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  • Unable to start sublime text

    - by Pramod
    I had been using Sublime Text 2 with no issues. I installed IDLE and now I'm unable to start Sublime Text. I tried uninstalling IDLE, but Sublime Text is still not starting. Here's the error: Unable to load libgdk-x11-2.0.so Unable to load gdk_cairo_create from libgdk-x11-2.0.so Unable to load gdk_cursor_new_for_display from libgdk-x11-2.0.so Unable to load gdk_cursor_unref from libgdk-x11-2.0.so Unable to load gdk_error_trap_pop from libgdk-x11-2.0.so Unable to load gdk_error_trap_push from libgdk-x11-2.0.so Unable to load gdk_input_add from libgdk-x11-2.0.so Unable to load gdk_input_remove from libgdk-x11-2.0.so Unable to load gdk_keymap_translate_keyboard_state from libgdk-x11-2.0.so Unable to load gdk_keyval_to_unicode from libgdk-x11-2.0.so Unable to load gdk_pixbuf_new_from_file from libgdk-x11-2.0.so Unable to load gdk_region_get_rectangles from libgdk-x11-2.0.so Unable to load gdk_screen_get_default from libgdk-x11-2.0.so Unable to load gdk_screen_get_display from libgdk-x11-2.0.so Unable to load gdk_screen_get_height from libgdk-x11-2.0.so Unable to load gdk_screen_get_rgb_colormap from libgdk-x11-2.0.so Unable to load gdk_screen_get_rgba_colormap from libgdk-x11-2.0.so Unable to load gdk_screen_get_root_window from libgdk-x11-2.0.so Unable to load gdk_screen_get_width from libgdk-x11-2.0.so Unable to load gdk_screen_get_n_monitors from libgdk-x11-2.0.so Unable to load gdk_screen_get_monitor_geometry from libgdk-x11-2.0.so Unable to load gdk_unicode_to_keyval from libgdk-x11-2.0.so Unable to load gdk_window_get_frame_extents from libgdk-x11-2.0.so Unable to load gdk_window_get_origin from libgdk-x11-2.0.so Unable to load gdk_window_get_state from libgdk-x11-2.0.so Unable to load gdk_window_invalidate_rect from libgdk-x11-2.0.so Unable to load gdk_window_set_cursor from libgdk-x11-2.0.so Unable to load gdk_window_move_resize from libgdk-x11-2.0.so Unable to load gdk_x11_display_get_xdisplay from libgdk-x11-2.0.so Unable to load gdk_x11_drawable_get_xid from libgdk-x11-2.0.so Unable to load gdk_x11_get_server_time from libgdk-x11-2.0.so Unable to load gdk_x11_get_xatom_by_name_for_display from libgdk-x11-2.0.so Unable to load gdk_x11_window_set_user_time from libgdk-x11-2.0.so Unable to load libgtk-x11-2.0.so Unable to load gtk_accel_group_new from libgtk-x11-2.0.so Unable to load gtk_accelerator_get_default_mod_mask from libgtk-x11-2.0.so Unable to load gtk_box_get_type from libgtk-x11-2.0.so Unable to load gtk_box_pack_start from libgtk-x11-2.0.so Unable to load gtk_check_menu_item_get_type from libgtk-x11-2.0.so Unable to load gtk_check_menu_item_new_with_label from libgtk-x11-2.0.so Unable to load gtk_check_menu_item_set_active from libgtk-x11-2.0.so Unable to load gtk_clipboard_clear from libgtk-x11-2.0.so Unable to load gtk_clipboard_get from libgtk-x11-2.0.so Unable to load gtk_clipboard_set_text from libgtk-x11-2.0.so Unable to load gtk_clipboard_set_with_data from libgtk-x11-2.0.so Unable to load gtk_clipboard_store from libgtk-x11-2.0.so Unable to load gtk_clipboard_wait_for_text from libgtk-x11-2.0.so Unable to load gtk_container_add from libgtk-x11-2.0.so Unable to load gtk_container_get_children from libgtk-x11-2.0.so Unable to load gtk_container_get_type from libgtk-x11-2.0.so Unable to load gtk_container_remove from libgtk-x11-2.0.so Unable to load gtk_dialog_add_button from libgtk-x11-2.0.so Unable to load gtk_dialog_get_type from libgtk-x11-2.0.so Unable to load gtk_dialog_run from libgtk-x11-2.0.so Unable to load gtk_dialog_set_default_response from libgtk-x11-2.0.so Unable to load gtk_drag_dest_set from libgtk-x11-2.0.so Unable to load gtk_drag_finish from libgtk-x11-2.0.so Unable to load gtk_file_chooser_add_filter from libgtk-x11-2.0.so Unable to load gtk_file_chooser_dialog_new from libgtk-x11-2.0.so Unable to load gtk_file_chooser_get_filename from libgtk-x11-2.0.so Unable to load gtk_file_chooser_get_files from libgtk-x11-2.0.so Unable to load gtk_file_chooser_get_type from libgtk-x11-2.0.so Unable to load gtk_file_chooser_set_current_folder from libgtk-x11-2.0.so Unable to load gtk_file_chooser_set_current_name from libgtk-x11-2.0.so Unable to load gtk_file_chooser_set_do_overwrite_confirmation from libgtk-x11-2.0.so Unable to load gtk_file_chooser_set_local_only from libgtk-x11-2.0.so Unable to load gtk_file_chooser_set_select_multiple from libgtk-x11-2.0.so Unable to load gtk_file_filter_add_pattern from libgtk-x11-2.0.so Unable to load gtk_file_filter_new from libgtk-x11-2.0.so Unable to load gtk_file_filter_set_name from libgtk-x11-2.0.so Unable to load gtk_get_current_event_time from libgtk-x11-2.0.so Unable to load gtk_im_context_filter_keypress from libgtk-x11-2.0.so Unable to load gtk_im_context_set_client_window from libgtk-x11-2.0.so Unable to load gtk_im_multicontext_new from libgtk-x11-2.0.so Unable to load gtk_init from libgtk-x11-2.0.so Unable to load gtk_main from libgtk-x11-2.0.so Unable to load gtk_main_quit from libgtk-x11-2.0.so Unable to load gtk_menu_attach_to_widget from libgtk-x11-2.0.so Unable to load gtk_menu_bar_new from libgtk-x11-2.0.so Unable to load gtk_menu_get_type from libgtk-x11-2.0.so Unable to load gtk_menu_item_get_label from libgtk-x11-2.0.so Unable to load gtk_menu_item_get_submenu from libgtk-x11-2.0.so Unable to load gtk_menu_item_get_type from libgtk-x11-2.0.so Unable to load gtk_menu_item_new_with_label from libgtk-x11-2.0.so Unable to load gtk_menu_item_set_label from libgtk-x11-2.0.so Unable to load gtk_menu_item_set_submenu from libgtk-x11-2.0.so Unable to load gtk_menu_item_set_use_underline from libgtk-x11-2.0.so Unable to load gtk_menu_new from libgtk-x11-2.0.so Unable to load gtk_menu_popup from libgtk-x11-2.0.so Unable to load gtk_menu_shell_append from libgtk-x11-2.0.so Unable to load gtk_menu_shell_get_type from libgtk-x11-2.0.so Unable to load gtk_message_dialog_new from libgtk-x11-2.0.so Unable to load gtk_message_dialog_new_with_markup from libgtk-x11-2.0.so Unable to load gtk_selection_data_get_uris from libgtk-x11-2.0.so Unable to load gtk_selection_data_set_text from libgtk-x11-2.0.so Unable to load gtk_separator_menu_item_new from libgtk-x11-2.0.so Unable to load gtk_settings_get_default from libgtk-x11-2.0.so Unable to load gtk_show_uri from libgtk-x11-2.0.so Unable to load gtk_vbox_new from libgtk-x11-2.0.so Unable to load gtk_widget_add_accelerator from libgtk-x11-2.0.so Unable to load gtk_widget_add_events from libgtk-x11-2.0.so Unable to load gtk_widget_destroy from libgtk-x11-2.0.so Unable to load gtk_widget_get_display from libgtk-x11-2.0.so Unable to load gtk_widget_get_parent from libgtk-x11-2.0.so Unable to load gtk_widget_get_screen from libgtk-x11-2.0.so Unable to load gtk_widget_get_type from libgtk-x11-2.0.so Unable to load gtk_widget_get_window from libgtk-x11-2.0.so Unable to load gtk_widget_grab_focus from libgtk-x11-2.0.so Unable to load gtk_widget_hide from libgtk-x11-2.0.so Unable to load gtk_widget_remove_accelerator from libgtk-x11-2.0.so Unable to load gtk_widget_set_app_paintable from libgtk-x11-2.0.so Unable to load gtk_widget_set_colormap from libgtk-x11-2.0.so Unable to load gtk_widget_set_double_buffered from libgtk-x11-2.0.so Unable to load gtk_widget_set_sensitive from libgtk-x11-2.0.so Unable to load gtk_widget_show from libgtk-x11-2.0.so Unable to load gtk_widget_show_all from libgtk-x11-2.0.so Unable to load gtk_window_add_accel_group from libgtk-x11-2.0.so Unable to load gtk_window_fullscreen from libgtk-x11-2.0.so Unable to load gtk_window_get_type from libgtk-x11-2.0.so Unable to load gtk_window_iconify from libgtk-x11-2.0.so Unable to load gtk_window_maximize from libgtk-x11-2.0.so Unable to load gtk_window_move from libgtk-x11-2.0.so Unable to load gtk_window_new from libgtk-x11-2.0.so Unable to load gtk_window_present_with_time from libgtk-x11-2.0.so Unable to load gtk_window_remove_accel_group from libgtk-x11-2.0.so Unable to load gtk_window_resize from libgtk-x11-2.0.so Unable to load gtk_window_set_default_icon_list from libgtk-x11-2.0.so Unable to load gtk_window_set_default_size from libgtk-x11-2.0.so Unable to load gtk_window_set_keep_above from libgtk-x11-2.0.so Unable to load gtk_window_set_modal from libgtk-x11-2.0.so Unable to load gtk_window_set_position from libgtk-x11-2.0.so Unable to load gtk_window_set_title from libgtk-x11-2.0.so Unable to load gtk_window_set_transient_for from libgtk-x11-2.0.so Unable to load gtk_window_set_type_hint from libgtk-x11-2.0.so Unable to load gtk_window_stick from libgtk-x11-2.0.so Unable to load gtk_window_unfullscreen from libgtk-x11-2.0.so Unable to load cairo_clip from libcairo.so Unable to load cairo_create from libcairo.so Unable to load cairo_destroy from libcairo.so Unable to load cairo_fill from libcairo.so Unable to load cairo_font_options_create from libcairo.so Unable to load cairo_font_options_destroy from libcairo.so Unable to load cairo_font_options_set_antialias from libcairo.so Unable to load cairo_get_source from libcairo.so Unable to load cairo_image_surface_create from libcairo.so Unable to load cairo_image_surface_create_for_data from libcairo.so Unable to load cairo_image_surface_get_data from libcairo.so Unable to load cairo_image_surface_get_format from libcairo.so Unable to load cairo_image_surface_get_height from libcairo.so Unable to load cairo_image_surface_get_stride from libcairo.so Unable to load cairo_image_surface_get_width from libcairo.so Unable to load cairo_line_to from libcairo.so Unable to load cairo_matrix_init_scale from libcairo.so Unable to load cairo_matrix_init_translate from libcairo.so Unable to load cairo_matrix_translate from libcairo.so Unable to load cairo_move_to from libcairo.so Unable to load cairo_paint_with_alpha from libcairo.so Unable to load cairo_pattern_set_extend from libcairo.so Unable to load cairo_pattern_set_matrix from libcairo.so Unable to load cairo_rectangle from libcairo.so Unable to load cairo_reset_clip from libcairo.so Unable to load cairo_restore from libcairo.so Unable to load cairo_save from libcairo.so Unable to load cairo_set_line_width from libcairo.so Unable to load cairo_set_operator from libcairo.so Unable to load cairo_set_source_rgb from libcairo.so Unable to load cairo_set_source_rgba from libcairo.so Unable to load cairo_set_source_surface from libcairo.so Unable to load cairo_stroke from libcairo.so Unable to load cairo_surface_destroy from libcairo.so Unable to load cairo_surface_flush from libcairo.so Unable to load cairo_translate from libcairo.so Unable to load cairo_scale from libcairo.so Unable to load pango_font_description_free from libpango-1.0.so Unable to load pango_font_description_new from libpango-1.0.so Unable to load pango_font_description_set_family from libpango-1.0.so Unable to load pango_font_description_set_size from libpango-1.0.so Unable to load pango_font_description_set_style from libpango-1.0.so Unable to load pango_font_description_set_weight from libpango-1.0.so Unable to load pango_font_get_metrics from libpango-1.0.so Unable to load pango_font_map_load_font from libpango-1.0.so Unable to load pango_font_metrics_get_ascent from libpango-1.0.so Unable to load pango_font_metrics_get_descent from libpango-1.0.so Unable to load pango_font_metrics_unref from libpango-1.0.so Unable to load pango_language_get_default from libpango-1.0.so Unable to load pango_layout_get_context from libpango-1.0.so Unable to load pango_layout_get_pixel_extents from libpango-1.0.so Unable to load pango_layout_set_font_description from libpango-1.0.so Unable to load pango_layout_set_text from libpango-1.0.so Unable to load pango_cairo_context_set_font_options from libpangocairo-1.0.so Unable to load pango_cairo_create_layout from libpangocairo-1.0.so Unable to load pango_font_map_create_context from libpangocairo-1.0.so Unable to load pango_cairo_font_map_get_default from libpangocairo-1.0.so Unable to load pango_cairo_show_layout from libpangocairo-1.0.so Unable to load pango_cairo_update_layout from libpangocairo-1.0.so Unable to load all required GTK functions Unable to init px Any solutions? Thanks!

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  • Big Data – Learning Basics of Big Data in 21 Days – Bookmark

    - by Pinal Dave
    Earlier this month I had a great time to write Bascis of Big Data series. This series received great response and lots of good comments I have received, I am going to follow up this basics series with further in-depth series in near future. Here is the consolidated blog post where you can find all the 21 days blog posts together. Bookmark this page for future reference. Big Data – Beginning Big Data – Day 1 of 21 Big Data – What is Big Data – 3 Vs of Big Data – Volume, Velocity and Variety – Day 2 of 21 Big Data – Evolution of Big Data – Day 3 of 21 Big Data – Basics of Big Data Architecture – Day 4 of 21 Big Data – Buzz Words: What is NoSQL – Day 5 of 21 Big Data – Buzz Words: What is Hadoop – Day 6 of 21 Big Data – Buzz Words: What is MapReduce – Day 7 of 21 Big Data – Buzz Words: What is HDFS – Day 8 of 21 Big Data – Buzz Words: Importance of Relational Database in Big Data World – Day 9 of 21 Big Data – Buzz Words: What is NewSQL – Day 10 of 21 Big Data – Role of Cloud Computing in Big Data – Day 11 of 21 Big Data – Operational Databases Supporting Big Data – RDBMS and NoSQL – Day 12 of 21 Big Data – Operational Databases Supporting Big Data – Key-Value Pair Databases and Document Databases – Day 13 of 21 Big Data – Operational Databases Supporting Big Data – Columnar, Graph and Spatial Database – Day 14 of 21 Big DataData Mining with Hive – What is Hive? – What is HiveQL (HQL)? – Day 15 of 21 Big Data – Interacting with Hadoop – What is PIG? – What is PIG Latin? – Day 16 of 21 Big Data – Interacting with Hadoop – What is Sqoop? – What is Zookeeper? – Day 17 of 21 Big Data – Basics of Big Data Analytics – Day 18 of 21 Big Data – How to become a Data Scientist and Learn Data Science? – Day 19 of 21 Big Data – Various Learning Resources – How to Start with Big Data? – Day 20 of 21 Big Data – Final Wrap and What Next – Day 21 of 21 Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • GlusterFs - high load 90-107% CPU

    - by Sara
    I try and try and try to performance and fix problem with gluster, i try all. I served on gluster webpages, php files, images etc. I have problem after update from 3.3.0 to 3.3.1. I try 3.4 when i think maybe fix it but still the same problem. I temporarily have 1 brick, but before upgrade will be fine. Config: Volume Name: ... Type: Replicate Volume ID: ... Status: Started Number of Bricks: 0 x 2 = 1 Transport-type: tcp Bricks: Brick1: ...:/... Options Reconfigured: cluster.stripe-block-size: 128KB performance.cache-max-file-size: 100MB performance.flush-behind: on performance.io-thread-count: 16 performance.cache-size: 256MB auth.allow: ... performance.cache-refresh-timeout: 5 performance.write-behind-window-size: 1024MB I use fuse, hmm "Maybe the high load is due to the unavailable brick" i think about it, but i cant find information on how to safely change type of volume. Maybe u know how?

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  • SQLAuthority News – A Successful Performance Tuning Seminar at Pune – Dec 4-5, 2010

    - by pinaldave
    This is report to my third of very successful seminar event on SQL Server Performance Tuning. SQL Server Performance Tuning Seminar in Colombo was oversubscribed with total of 35 attendees. You can read the details over here SQLAuthority News – SQL Server Performance Optimizations Seminar – Grand Success – Colombo, Sri Lanka – Oct 4 – 5, 2010. SQL Server Performance Tuning Seminar in Hyderabad was oversubscribed with total of 25 attendees. You can read the details over here SQL SERVER – A Successful Performance Tuning Seminar – Hyderabad – Nov 27-28, 2010. The same Seminar was offered in Pune on December 4,-5, 2010. We had another successful seminar with lots of performance talk. This seminar was attended by 30 attendees. The best part of the seminar was that along with the our agenda, we have talked about following very interesting concepts. Deadlocks Detection and Removal Dynamic SQL and Inline Code SQL Optimizations Multiple OR conditions and performance tuning Dynamic Search Condition Building and Improvement Memory Cache and Improvement Bottleneck Detections – Memory, CPU and IO Beginning Performance Tuning on Production Parametrization Improving already Super Fast Queries Convenience vs. Performance Proper way to create Indexes Hints and Disadvantages I had great time doing the seminar and sharing my performance tricks with all. The highlight of this seminar was I have explained the attendees, how I begin doing performance tuning when I go for Performance Tuning Consultations.   Pinal Dave at SQL Performance Tuning Seminar SQL Server Performance Tuning Seminar Pinal Dave at SQL Performance Tuning Seminar Pinal Dave at SQL Performance Tuning Seminar SQL Server Performance Tuning Seminar SQL Server Performance Tuning Seminar This seminar series are 100% demo oriented and no usual PowerPoint talk. They are created from my experiences of various organizations for performance tuning. I am not planning any more seminar this year as it was great but I am booked currently for next 60 days at various performance tuning engagements. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, SQL Training, SQLAuthority News, T SQL, Technology

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  • Data Quality Through Data Governance

    Data Quality Governance Data quality is very important to every organization, bad data cost an organization time, money, and resources that could be prevented if the proper governance was put in to place.  Data Governance Program Criteria: Support from Executive Management and all Business Units Data Stewardship Program  Cross Functional Team of Data Stewards Data Governance Committee Quality Structured Data It should go without saying but any successful project in today’s business world must get buy in from executive management and all stakeholders involved with the project. If management does not fully support a project because they see it is in there and the company’s best interest then they will remove/eliminate funding, resources and allocated time to work on the project. In essence they can render a project dead until it is official killed by the business. In addition, buy in from stake holders is also very important because they can cause delays increased spending in time, money and resources because they do not support a project. Data Stewardship programs are administered by a data steward manager who primary focus is to support, train and manage a cross functional data stewards team. A cross functional team of data stewards are pulled from various departments act to ensure that all systems work to ensure that an organization’s goals are achieved. Typically, data stewards are subject matter experts that act as mediators between their respective departments and IT. Data Quality Procedures Data Governance Committees are composed of data stewards, Upper management, IT Leadership and various subject matter experts depending on a company. The primary goal of this committee is to define strategic goals, coordinate activities, set data standards and offer data guidelines for the business. Data Quality Policies In 1997, Claudia Imhoff defined a Data Stewardship’s responsibility as to approve business naming standards, develop consistent data definitions, determine data aliases, develop standard calculations and derivations, document the business rules of the corporation, monitor the quality of the data in the data warehouse, define security requirements, and so forth. She further explains data stewards responsible for creating and enforcing polices on the following but not limited to issues. Resolving Data Integration Issues Determining Data Security Documenting Data Definitions, Calculations, Summarizations, etc. Maintaining/Updating Business Rules Analyzing and Improving Data Quality

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  • SQL SERVER – Advanced Data Quality Services with Melissa Data – Azure Data Market

    - by pinaldave
    There has been much fanfare over the new SQL Server 2012, and especially around its new companion product Data Quality Services (DQS). Among the many new features is the addition of this integrated knowledge-driven product that enables data stewards everywhere to profile, match, and cleanse data. In addition to the homegrown rules that data stewards can design and implement, there are also connectors to third party providers that are hosted in the Azure Datamarket marketplace.  In this review, I leverage SQL Server 2012 Data Quality Services, and proceed to subscribe to a third party data cleansing product through the Datamarket to showcase this unique capability. Crucial Questions For the purposes of the review, I used a database I had in an Excel spreadsheet with name and address information. Upon a cursory inspection, there are miscellaneous problems with these records; some addresses are missing ZIP codes, others missing a city, and some records are slightly misspelled or have unparsed suites. With DQS, I can easily add a knowledge base to help standardize my values, such as for state abbreviations. But how do I know that my address is correct? And if my address is not correct, what should it be corrected to? The answer lies in a third party knowledge base by the acknowledged USPS certified address accuracy experts at Melissa Data. Reference Data Services Within DQS there is a handy feature to actually add reference data from many different third-party Reference Data Services (RDS) vendors. DQS simplifies the processes of cleansing, standardizing, and enriching data through custom rules and through service providers from the Azure Datamarket. A quick jump over to the Datamarket site shows me that there are a handful of providers that offer data directly through Data Quality Services. Upon subscribing to these services, one can attach a DQS domain or composite domain (fields in a record) to a reference data service provider, and begin using it to cleanse, standardize, and enrich that data. Besides what I am looking for (address correction and enrichment), it is possible to subscribe to a host of other services including geocoding, IP address reference, phone checking and enrichment, as well as name parsing, standardization, and genderization.  These capabilities extend the data quality that DQS has natively by quite a bit. For my current address correction review, I needed to first sign up to a reference data provider on the Azure Data Market site. For this example, I used Melissa Data’s Address Check Service. They offer free one-month trials, so if you wish to follow along, or need to add address quality to your own data, I encourage you to sign up with them. Once I subscribed to the desired Reference Data Provider, I navigated my browser to the Account Keys within My Account to view the generated account key, which I then inserted into the DQS Client – Configuration under the Administration area. Step by Step to Guide That was all it took to hook in the subscribed provider -Melissa Data- directly to my DQS Client. The next step was for me to attach and map in my Reference Data from the newly acquired reference data provider, to a domain in my knowledge base. On the DQS Client home screen, I selected “New Knowledge Base” under Knowledge Base Management on the left-hand side of the home screen. Under New Knowledge Base, I typed a Name and description of my new knowledge base, then proceeded to the Domain Management screen. Here I established a series of domains (fields) and then linked them all together as a composite domain (record set). Using the Create Domain button, I created the following domains according to the fields in my incoming data: Name Address Suite City State Zip I added a Suite column in my domain because Melissa Data has the ability to return missing Suites based on last name or company. And that’s a great benefit of using these third party providers, as they have data that the data steward would not normally have access to. The bottom line is, with these third party data providers, I can actually improve my data. Next, I created a composite domain (fulladdress) and added the (field) domains into the composite domain. This essentially groups our address fields together in a record to facilitate the full address cleansing they perform. I then selected my newly created composite domain and under the Reference Data tab, added my third party reference data provider –Melissa Data’s Address Check- and mapped in each domain that I had to the provider’s Schema. Now that my composite domain has been married to the Reference Data service, I can take the newly published knowledge base and create a project to cleanse and enrich my data. My next task was to create a new Data Quality project, mapping in my data source and matching it to the appropriate domain column, and then kick off the verification process. It took just a few minutes with some progress indicators indicating that it was working. When the process concluded, there was a helpful set of tabs that place the response records into categories: suggested; new; invalid; corrected (automatically); and correct. Accepting the suggestions provided by  Melissa Data allowed me to clean up all the records and flag the invalid ones. It is very apparent that DQS makes address data quality simplistic for any IT professional. Final Note As I have shown, DQS makes data quality very easy. Within minutes I was able to set up a data cleansing and enrichment routine within my data quality project, and ensure that my address data was clean, verified, and standardized against real reference data. As reviewed here, it’s easy to see how both SQL Server 2012 and DQS work to take what used to require a highly skilled developer, and empower an average business or database person to consume external services and clean data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Utility, T SQL, Technology Tagged: DQS

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  • Big Data – Is Big Data Relevant to me? – Big Data Questionnaires – Guest Post by Vinod Kumar

    - by Pinal Dave
    This guest post is by Vinod Kumar. Vinod Kumar has worked with SQL Server extensively since joining the industry over a decade ago. Working on various versions of SQL Server 7.0, Oracle 7.3 and other database technologies – he now works with the Microsoft Technology Center (MTC) as a Technology Architect. Let us read the blog post in Vinod’s own voice. I think the series from Pinal is a good one for anyone planning to start on Big Data journey from the basics. In my daily customer interactions this buzz of “Big Data” always comes up, I react generally saying – “Sir, do you really have a ‘Big Data’ problem or do you have a big Data problem?” Generally, there is a silence in the air when I ask this question. Data is everywhere in organizations – be it big data, small data, all data and for few it is bad data which is same as no data :). Wow, don’t discount me as someone who opposes “Big Data”, I am a big supporter as much as I am a critic of the abuse of this term by the people. In this post, I wanted to let my mind flow so that you can also think in the direction I want you to see these concepts. In any case, this is not an exhaustive dump of what is in my mind – but you will surely get the drift how I am going to question Big Data terms from customers!!! Is Big Data Relevant to me? Many of my customers talk to me like blank whiteboard with no idea – “why Big Data”. They want to jump into the bandwagon of technology and they want to decipher insights from their unexplored data a.k.a. unstructured data with structured data. So what are these industry scenario’s that come to mind? Here are some of them: Financials Fraud detection: Banks and Credit cards are monitoring your spending habits on real-time basis. Customer Segmentation: applies in every industry from Banking to Retail to Aviation to Utility and others where they deal with end customer who consume their products and services. Customer Sentiment Analysis: Responding to negative brand perception on social or amplify the positive perception. Sales and Marketing Campaign: Understand the impact and get closer to customer delight. Call Center Analysis: attempt to take unstructured voice recordings and analyze them for content and sentiment. Medical Reduce Re-admissions: How to build a proactive follow-up engagements with patients. Patient Monitoring: How to track Inpatient, Out-Patient, Emergency Visits, Intensive Care Units etc. Preventive Care: Disease identification and Risk stratification is a very crucial business function for medical. Claims fraud detection: There is no precise dollars that one can put here, but this is a big thing for the medical field. Retail Customer Sentiment Analysis, Customer Care Centers, Campaign Management. Supply Chain Analysis: Every sensors and RFID data can be tracked for warehouse space optimization. Location based marketing: Based on where a check-in happens retail stores can be optimize their marketing. Telecom Price optimization and Plans, Finding Customer churn, Customer loyalty programs Call Detail Record (CDR) Analysis, Network optimizations, User Location analysis Customer Behavior Analysis Insurance Fraud Detection & Analysis, Pricing based on customer Sentiment Analysis, Loyalty Management Agents Analysis, Customer Value Management This list can go on to other areas like Utility, Manufacturing, Travel, ITES etc. So as you can see, there are obviously interesting use cases for each of these industry verticals. These are just representative list. Where to start? A lot of times I try to quiz customers on a number of dimensions before starting a Big Data conversation. Are you getting the data you need the way you want it and in a timely manner? Can you get in and analyze the data you need? How quickly is IT to respond to your BI Requests? How easily can you get at the data that you need to run your business/department/project? How are you currently measuring your business? Can you get the data you need to react WITHIN THE QUARTER to impact behaviors to meet your numbers or is it always “rear-view mirror?” How are you measuring: The Brand Customer Sentiment Your Competition Your Pricing Your performance Supply Chain Efficiencies Predictive product / service positioning What are your key challenges of driving collaboration across your global business?  What the challenges in innovation? What challenges are you facing in getting more information out of your data? Note: Garbage-in is Garbage-out. Hold good for all reporting / analytics requirements Big Data POCs? A number of customers get into the realm of setting a small team to work on Big Data – well it is a great start from an understanding point of view, but I tend to ask a number of other questions to such customers. Some of these common questions are: To what degree is your advanced analytics (natural language processing, sentiment analysis, predictive analytics and classification) paired with your Big Data’s efforts? Do you have dedicated resources exploring the possibilities of advanced analytics in Big Data for your business line? Do you plan to employ machine learning technology while doing Advanced Analytics? How is Social Media being monitored in your organization? What is your ability to scale in terms of storage and processing power? Do you have a system in place to sort incoming data in near real time by potential value, data quality, and use frequency? Do you use event-driven architecture to manage incoming data? Do you have specialized data services that can accommodate different formats, security, and the management requirements of multiple data sources? Is your organization currently using or considering in-memory analytics? To what degree are you able to correlate data from your Big Data infrastructure with that from your enterprise data warehouse? Have you extended the role of Data Stewards to include ownership of big data components? Do you prioritize data quality based on the source system (that is Facebook/Twitter data has lower quality thresholds than radio frequency identification (RFID) for a tracking system)? Do your retention policies consider the different legal responsibilities for storing Big Data for a specific amount of time? Do Data Scientists work in close collaboration with Data Stewards to ensure data quality? How is access to attributes of Big Data being given out in the organization? Are roles related to Big Data (Advanced Analyst, Data Scientist) clearly defined? How involved is risk management in the Big Data governance process? Is there a set of documented policies regarding Big Data governance? Is there an enforcement mechanism or approach to ensure that policies are followed? Who is the key sponsor for your Big Data governance program? (The CIO is best) Do you have defined policies surrounding the use of social media data for potential employees and customers, as well as the use of customer Geo-location data? How accessible are complex analytic routines to your user base? What is the level of involvement with outside vendors and third parties in regard to the planning and execution of Big Data projects? What programming technologies are utilized by your data warehouse/BI staff when working with Big Data? These are some of the important questions I ask each customer who is actively evaluating Big Data trends for their organizations. These questions give you a sense of direction where to start, what to use, how to secure, how to analyze and more. Sign off Any Big data is analysis is incomplete without a compelling story. The best way to understand this is to watch Hans Rosling – Gapminder (2:17 to 6:06) videos about the third world myths. Don’t get overwhelmed with the Big Data buzz word, the destination to what your data speaks is important. In this blog post, we did not particularly look at any Big Data technologies. This is a set of questionnaire one needs to keep in mind as they embark their journey of Big Data. I did write some of the basics in my blog: Big Data – Big Hype yet Big Opportunity. Do let me know if these questions make sense?  Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Big Data – Basics of Big Data Architecture – Day 4 of 21

    - by Pinal Dave
    In yesterday’s blog post we understood how Big Data evolution happened. Today we will understand basics of the Big Data Architecture. Big Data Cycle Just like every other database related applications, bit data project have its development cycle. Though three Vs (link) for sure plays an important role in deciding the architecture of the Big Data projects. Just like every other project Big Data project also goes to similar phases of the data capturing, transforming, integrating, analyzing and building actionable reporting on the top of  the data. While the process looks almost same but due to the nature of the data the architecture is often totally different. Here are few of the question which everyone should ask before going ahead with Big Data architecture. Questions to Ask How big is your total database? What is your requirement of the reporting in terms of time – real time, semi real time or at frequent interval? How important is the data availability and what is the plan for disaster recovery? What are the plans for network and physical security of the data? What platform will be the driving force behind data and what are different service level agreements for the infrastructure? This are just basic questions but based on your application and business need you should come up with the custom list of the question to ask. As I mentioned earlier this question may look quite simple but the answer will not be simple. When we are talking about Big Data implementation there are many other important aspects which we have to consider when we decide to go for the architecture. Building Blocks of Big Data Architecture It is absolutely impossible to discuss and nail down the most optimal architecture for any Big Data Solution in a single blog post, however, we can discuss the basic building blocks of big data architecture. Here is the image which I have built to explain how the building blocks of the Big Data architecture works. Above image gives good overview of how in Big Data Architecture various components are associated with each other. In Big Data various different data sources are part of the architecture hence extract, transform and integration are one of the most essential layers of the architecture. Most of the data is stored in relational as well as non relational data marts and data warehousing solutions. As per the business need various data are processed as well converted to proper reports and visualizations for end users. Just like software the hardware is almost the most important part of the Big Data Architecture. In the big data architecture hardware infrastructure is extremely important and failure over instances as well as redundant physical infrastructure is usually implemented. NoSQL in Data Management NoSQL is a very famous buzz word and it really means Not Relational SQL or Not Only SQL. This is because in Big Data Architecture the data is in any format. It can be unstructured, relational or in any other format or from any other data source. To bring all the data together relational technology is not enough, hence new tools, architecture and other algorithms are invented which takes care of all the kind of data. This is collectively called NoSQL. Tomorrow Next four days we will answer the Buzz Words – Hadoop. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Oracle Application Server Performance Monitoring and Tuning (CPU load high)

    - by Berkay
    Oracle Application Server Performance Monitoring and Tuning (CPU load high) i have just hired by a company and my boss give me a performance issue to solve as soon as possible. I don't have any experience with the Java EE before at the server side. Let me begin what i learned about the system and still couldn't find the solution: We have an Oracle Application Server (10.1.) and Oracle Database server (9.2.), the software guys wrote a kind of big J2EE project (X project) using specifically JSF 1.2 with Ajax which is only used in this project. They actively use PL/SQL in their code. So, we started the application server (Solaris machine), everything seems OK. users start using the app starting Monday from different locations (app 200 have user accounts,i just checked and see that the connection pool is set right, the session are active only 15 minutes). After sometime (2 days) CPU utilization gets high,%60, at night it is still same nothing changed (the online user amount is nearly 1 or 2 at this time), even it starts using the CPU allocated for other applications on the same server because they freed If we don't restart the server, the utilization becomes %90 following 2 days, application is so slow that end users starts calling. The main problem is software engineers say that code is clear, and the System and DBA managers say that we have the correct configuration,the other applications seems OK why this problem happens only for X application. I start copying the DB to a test platform and upgrade it to the latest version, also did in same with the application server (Weblogic) if there is a bug or not. i only tested by myself only one user and weblogic admin panel i can track the threads and dump them. i noticed that there are some threads showing as a hogging. when i checked the manuals and control the trace i see that it directs me the line number where PL/SQL code is called from a .java file. The software eng. says that yes we have really complex PL/SQL codes but what's the relation with Application server? this is the problem of DB server, i guess they're right... I know the question has many holes, i'd like to give more in detail but i appreciate the way you guide me. Thanks in advance ... Edit: The server both in CPU and Memory enough to run more complex applications

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  • Big Data – Evolution of Big Data – Day 3 of 21

    - by Pinal Dave
    In yesterday’s blog post we answered what is the Big Data. Today we will understand why and how the evolution of Big Data has happened. Though the answer is very simple, I would like to tell it in the form of a history lesson. Data in Flat File In earlier days data was stored in the flat file and there was no structure in the flat file.  If any data has to be retrieved from the flat file it was a project by itself. There was no possibility of retrieving the data efficiently and data integrity has been just a term discussed without any modeling or structure around. Database residing in the flat file had more issues than we would like to discuss in today’s world. It was more like a nightmare when there was any data processing involved in the application. Though, applications developed at that time were also not that advanced the need of the data was always there and there was always need of proper data management. Edgar F Codd and 12 Rules Edgar Frank Codd was a British computer scientist who, while working for IBM, invented the relational model for database management, the theoretical basis for relational databases. He presented 12 rules for the Relational Database and suddenly the chaotic world of the database seems to see discipline in the rules. Relational Database was a promising land for all the unstructured database users. Relational Database brought into the relationship between data as well improved the performance of the data retrieval. Database world had immediately seen a major transformation and every single vendors and database users suddenly started to adopt the relational database models. Relational Database Management Systems Since Edgar F Codd proposed 12 rules for the RBDMS there were many different vendors who started them to build applications and tools to support the relationship between database. This was indeed a learning curve for many of the developer who had never worked before with the modeling of the database. However, as time passed by pretty much everybody accepted the relationship of the database and started to evolve product which performs its best with the boundaries of the RDBMS concepts. This was the best era for the databases and it gave the world extreme experts as well as some of the best products. The Entity Relationship model was also evolved at the same time. In software engineering, an Entity–relationship model (ER model) is a data model for describing a database in an abstract way. Enormous Data Growth Well, everything was going fine with the RDBMS in the database world. As there were no major challenges the adoption of the RDBMS applications and tools was pretty much universal. There was a race at times to make the developer’s life much easier with the RDBMS management tools. Due to the extreme popularity and easy to use system pretty much every data was stored in the RDBMS system. New age applications were built and social media took the world by the storm. Every organizations was feeling pressure to provide the best experience for their users based the data they had with them. While this was all going on at the same time data was growing pretty much every organization and application. Data Warehousing The enormous data growth now presented a big challenge for the organizations who wanted to build intelligent systems based on the data and provide near real time superior user experience to their customers. Various organizations immediately start building data warehousing solutions where the data was stored and processed. The trend of the business intelligence becomes the need of everyday. Data was received from the transaction system and overnight was processed to build intelligent reports from it. Though this is a great solution it has its own set of challenges. The relational database model and data warehousing concepts are all built with keeping traditional relational database modeling in the mind and it still has many challenges when unstructured data was present. Interesting Challenge Every organization had expertise to manage structured data but the world had already changed to unstructured data. There was intelligence in the videos, photos, SMS, text, social media messages and various other data sources. All of these needed to now bring to a single platform and build a uniform system which does what businesses need. The way we do business has also been changed. There was a time when user only got the features what technology supported, however, now users ask for the feature and technology is built to support the same. The need of the real time intelligence from the fast paced data flow is now becoming a necessity. Large amount (Volume) of difference (Variety) of high speed data (Velocity) is the properties of the data. The traditional database system has limits to resolve the challenges this new kind of the data presents. Hence the need of the Big Data Science. We need innovation in how we handle and manage data. We need creative ways to capture data and present to users. Big Data is Reality! Tomorrow In tomorrow’s blog post we will try to answer discuss Basics of Big Data Architecture. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • DB2 insert performance - How to measure

    - by svrist
    [From stackoverflow] Im trying to find a way to speedup my inserts to a DB2 9.7.1 (ubuntu linux) Im watching vmstat and trying to gather some statistics via the db2 get snapshot commands but im not able to figure out which numbers im looking for to be able to see where the trouble is. I've read lits of stuff like http://www.eggheadcafe.com/software/aspnet/35692526/question-multiple-row-in.aspx, and http://www.ibm.com/developerworks/data/library/tips/dm-0403wilkins/ and tricks like ALTER TABLE lalala APPEND ON works somewhat (the difference between a dd if=/dev/zero and insert is still a factor 10) but I would like to be able to find the counters or other performance indicators that actually show why it makes sense to use those tricks. For example: What is the metric called that shows me that it is buffer pages allocation (FSCR stuff) that is the problem Where do I see that the insert time is hampered by clustered indexes? I find db2top very useful but im still searching for more direct view of "this is your bottleneck" methods

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  • How I use PowerShell to collect Performance Counter data

    - by AaronBertrand
    In a current project, I need to collect performance counters from a set of virtual machines that are performing different tasks and running a variety of workloads. In a similar project last year, I used LogMan to collect performance data. This time I decided to try PowerShell because, well, all the kids are doing it, I felt a little passé, and a lot of the other tasks in this project (such as building out VMs and running workloads) were already being accomplished via PowerShell. And after all, I...(read more)

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  • How I use PowerShell to collect Performance Counter data

    - by AaronBertrand
    In a current project, I need to collect performance counters from a set of virtual machines that are performing different tasks and running a variety of workloads. In a similar project last year, I used LogMan to collect performance data. This time I decided to try PowerShell because, well, all the kids are doing it, I felt a little passé, and a lot of the other tasks in this project (such as building out VMs and running workloads) were already being accomplished via PowerShell. And after all, I...(read more)

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  • Working with Temporal Data in SQL Server

    - by Dejan Sarka
    My third Pluralsight course, Working with Temporal Data in SQL Server, is published. I am really proud on the second part of the course, where I discuss optimization of temporal queries. This was a nearly impossible task for decades. First solutions appeared only lately. I present all together six solutions (and one more that is not a solution), and I invented four of them. http://pluralsight.com/training/Courses/TableOfContents/working-with-temporal-data-sql-server

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  • New Communications Industry Data Model with "Factory Installed" Predictive Analytics using Oracle Da

    - by charlie.berger
    Oracle Introduces Oracle Communications Data Model to Provide Actionable Insight for Communications Service Providers   We've integrated pre-installed analytical methodologies with the new Oracle Communications Data Model to deliver automated, simple, yet powerful predictive analytics solutions for customers.  Churn, sentiment analysis, identifying customer segments - all things that can be anticipated and hence, preconcieved and implemented inside an applications.  Read on for more information! TM Forum Management World, Nice, France - 18 May 2010 News Facts To help communications service providers (CSPs) manage and analyze rapidly growing data volumes cost effectively, Oracle today introduced the Oracle Communications Data Model. With the Oracle Communications Data Model, CSPs can achieve rapid time to value by quickly implementing a standards-based enterprise data warehouse that features communications industry-specific reporting, analytics and data mining. The combination of the Oracle Communications Data Model, Oracle Exadata and the Oracle Business Intelligence (BI) Foundation represents the most comprehensive data warehouse and BI solution for the communications industry. Also announced today, Hong Kong Broadband Network enhanced their data warehouse system, going live on Oracle Communications Data Model in three months. The leading provider increased its subscriber base by 37 percent in six months and reduced customer churn to less than one percent. Product Details Oracle Communications Data Model provides industry-specific schema and embedded analytics that address key areas such as customer management, marketing segmentation, product development and network health. CSPs can efficiently capture and monitor critical data and transform it into actionable information to support development and delivery of next-generation services using: More than 1,300 industry-specific measurements and key performance indicators (KPIs) such as network reliability statistics, provisioning metrics and customer churn propensity. Embedded OLAP cubes for extremely fast dimensional analysis of business information. Embedded data mining models for sophisticated trending and predictive analysis. Support for multiple lines of business, such as cable, mobile, wireline and Internet, which can be easily extended to support future requirements. With Oracle Communications Data Model, CSPs can jump start the implementation of a communications data warehouse in line with communications-industry standards including the TM Forum Information Framework (SID), formerly known as the Shared Information Model. Oracle Communications Data Model is optimized for any Oracle Database 11g platform, including Oracle Exadata, which can improve call data record query performance by 10x or more. Supporting Quotes "Oracle Communications Data Model covers a wide range of business areas that are relevant to modern communications service providers and is a comprehensive solution - with its data model and pre-packaged templates including BI dashboards, KPIs, OLAP cubes and mining models. It helps us save a great deal of time in building and implementing a customized data warehouse and enables us to leverage the advanced analytics quickly and more effectively," said Yasuki Hayashi, executive manager, NTT Comware Corporation. "Data volumes will only continue to grow as communications service providers expand next-generation networks, deploy new services and adopt new business models. They will increasingly need efficient, reliable data warehouses to capture key insights on data such as customer value, network value and churn probability. With the Oracle Communications Data Model, Oracle has demonstrated its commitment to meeting these needs by delivering data warehouse tools designed to fill communications industry-specific needs," said Elisabeth Rainge, program director, Network Software, IDC. "The TM Forum Conformance Mark provides reassurance to customers seeking standards-based, and therefore, cost-effective and flexible solutions. TM Forum is extremely pleased to work with Oracle to certify its Oracle Communications Data Model solution. Upon successful completion, this certification will represent the broadest and most complete implementation of the TM Forum Information Framework to date, with more than 130 aggregate business entities," said Keith Willetts, chairman and chief executive officer, TM Forum. Supporting Resources Oracle Communications Oracle Communications Data Model Data Sheet Oracle Communications Data Model Podcast Oracle Data Warehousing Oracle Communications on YouTube Oracle Communications on Delicious Oracle Communications on Facebook Oracle Communications on Twitter Oracle Communications on LinkedIn Oracle Database on Twitter The Data Warehouse Insider Blog

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  • Big Data – How to become a Data Scientist and Learn Data Science? – Day 19 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned the importance of the analytics in Big Data Story. In this article we will understand how to become a Data Scientist for Big Data Story. Data Scientist is a new buzz word, everyone seems to be wanting to become Data Scientist. Let us go over a few key topics related to Data Scientist in this blog post. First of all we will understand what is a Data Scientist. In the new world of Big Data, I see pretty much everyone wants to become Data Scientist and there are lots of people I have already met who claims that they are Data Scientist. When I ask what is their role, I have got a wide variety of answers. What is Data Scientist? Data scientists are the experts who understand various aspects of the business and know how to strategies data to achieve the business goals. They should have a solid foundation of various data algorithms, modeling and statistics methodology. What do Data Scientists do? Data scientists understand the data very well. They just go beyond the regular data algorithms and builds interesting trends from available data. They innovate and resurrect the entire new meaning from the existing data. They are artists in disguise of computer analyst. They look at the data traditionally as well as explore various new ways to look at the data. Data Scientists do not wait to build their solutions from existing data. They think creatively, they think before the data has entered into the system. Data Scientists are visionary experts who understands the business needs and plan ahead of the time, this tremendously help to build solutions at rapid speed. Besides being data expert, the major quality of Data Scientists is “curiosity”. They always wonder about what more they can get from their existing data and how to get maximum out of future incoming data. Data Scientists do wonders with the data, which goes beyond the job descriptions of Data Analysist or Business Analysist. Skills Required for Data Scientists Here are few of the skills a Data Scientist must have. Expert level skills with statistical tools like SAS, Excel, R etc. Understanding Mathematical Models Hands-on with Visualization Tools like Tableau, PowerPivots, D3. j’s etc. Analytical skills to understand business needs Communication skills On the technology front any Data Scientists should know underlying technologies like (Hadoop, Cloudera) as well as their entire ecosystem (programming language, analysis and visualization tools etc.) . Remember that for becoming a successful Data Scientist one require have par excellent skills, just having a degree in a relevant education field will not suffice. Final Note Data Scientists is indeed very exciting job profile. As per research there are not enough Data Scientists in the world to handle the current data explosion. In near future Data is going to expand exponentially, and the need of the Data Scientists will increase along with it. It is indeed the job one should focus if you like data and science of statistics. Courtesy: emc Tomorrow In tomorrow’s blog post we will discuss about various Big Data Learning resources. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Unstructured Data - The future of Data Administration

    Some have claimed that there is a problem with the way data is currently managed using the relational paradigm do to the rise of unstructured data in modern business. PCMag.com defines unstructured data as data that does not reside in a fixed location. They further explain that unstructured data refers to data in a free text form that is not bound to any specific structure. With the rise of unstructured data in the form of emails, spread sheets, images and documents the critics have a right to argue that the relational paradigm is not as effective as the object oriented data paradigm in managing this type of data. The relational paradigm relies heavily on structure and relationships in and between items of data. This type of paradigm works best in a relation database management system like Microsoft SQL, MySQL, and Oracle because data is forced to conform to a structure in the form of tables and relations can be derived from the existence of one or more tables. These critics also claim that database administrators have not kept up with reality because their primary focus in regards to data administration deals with structured data and the relational paradigm. The relational paradigm was developed in the 1970’s as a way to improve data management when compared to standard flat files. Little has changed since then, and modern database administrators need to know more than just how to handle structured data. That is why critics claim that today’s data professionals do not have the proper skills in order to store and maintain data for modern systems when compared to the skills of system designers, programmers , software engineers, and data designers  due to the industry trend of object oriented design and development. I think that they are wrong. I do not disagree that the industry is moving toward an object oriented approach to development with the potential to use more of an object oriented approach to data.   However, I think that it is business itself that is limiting database administrators from changing how data is stored because of the potential costs, and impact that might occur by altering any part of stored data. Furthermore, database administrators like all technology workers constantly are trying to improve their technical skills in order to excel in their job, so I think that accusing data professional is not just when the root cause of the lack of innovation is controlled by business, and it is business that will suffer for their inability to keep up with technology. One way for database professionals to better prepare for the future of database management is start working with data in the form of objects and so that they can extract data from the objects so that the stored information within objects can be used in relation to the data stored in a using the relational paradigm. Furthermore, I think the use of pattern matching will increase with the increased use of unstructured data because object can be selected, filtered and altered based on the existence of a pattern found within an object.

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  • Developing an analytics's system processing large amounts of data - where to start

    - by Ryan
    Imagine you're writing some sort of Web Analytics system - you're recording raw page hits along with some extra things like tagging cookies etc and then producing stats such as Which pages got most traffic over a time period Which referers sent most traffic Goals completed (goal being a view of a particular page) And more advanced things like which referers sent the most number of vistors who later hit a goal. The naieve way of approaching this would be to throw it in a relational database and run queries over it - but that won't scale. You could pre-calculate everything (have a queue of incoming 'hits' and use to update report tables) - but what if you later change a goal - how could you efficiently re-calculate just the data that would be effected. Obviously this has been done before ;) so any tips on where to start, methods & examples, architecture, technologies etc.

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  • High CPU load - Ubuntu 14.04

    - by watt
    I noticed that sometimes when browsing (with other processes in the background), I get very high CPU load for the browser process (over 100%) and the computer becomes really slow. I tried switching from Firefox (with just a few extensions) to Chromium, but same thing happens without me visiting graphics-intense sites, flash sites or anything like that. I also noticed python or node (when running "make") produce the same high CPU load from time to time so this is not necessarily browser-related. When I only have a browser open, it doesn't seem to happen and everything is fine in Windows 7. I switched from unity to gnome3 with no effect. Specs: lenovo w510 (4gb RAM, i7 q820 @ 1.73) + up to date Ubuntu 14.04 64bit. Printscreen: http://imgur.com/8MZJNKC Do you guys have any idea why this might happen? Please let me know if there's other info you need. Thanks!

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  • alsHigh CPU load - Ubuntu 14.04

    - by watt
    I noticed that sometimes when browsing (with other processes in the background), I get very high CPU load for the browser process (over 100%) and the computer becomes really slow. I tried switching from Firefox (with just a few extensions) to Chromium, but same thing happens without me visiting graphics-intense sites, flash sites or anything like that. I also noticed python or node (when running "make") produce the same high CPU load from time to time so this is not necessarily browser-related. When I only have a browser open, it doesn't seem to happen and everything is fine in Windows 7. I switched from unity to gnome3 with no effect. Specs: lenovo w510 (4gb RAM, i7 q820 @ 1.73) + up to date Ubuntu 14.04 64bit. Printscreen: http://imgur.com/8MZJNKC Do you guys have any idea why this might happen? Please let me know if there's other info you need. Thanks!

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  • Big Data – Beginning Big Data – Day 1 of 21

    - by Pinal Dave
    What is Big Data? I want to learn Big Data. I have no clue where and how to start learning about it. Does Big Data really means data is big? What are the tools and software I need to know to learn Big Data? I often receive questions which I mentioned above. They are good questions and honestly when we search online, it is hard to find authoritative and authentic answers. I have been working with Big Data and NoSQL for a while and I have decided that I will attempt to discuss this subject over here in the blog. In the next 21 days we will understand what is so big about Big Data. Big Data – Big Thing! Big Data is becoming one of the most talked about technology trends nowadays. The real challenge with the big organization is to get maximum out of the data already available and predict what kind of data to collect in the future. How to take the existing data and make it meaningful that it provides us accurate insight in the past data is one of the key discussion points in many of the executive meetings in organizations. With the explosion of the data the challenge has gone to the next level and now a Big Data is becoming the reality in many organizations. Big Data – A Rubik’s Cube I like to compare big data with the Rubik’s cube. I believe they have many similarities. Just like a Rubik’s cube it has many different solutions. Let us visualize a Rubik’s cube solving challenge where there are many experts participating. If you take five Rubik’s cube and mix up the same way and give it to five different expert to solve it. It is quite possible that all the five people will solve the Rubik’s cube in fractions of the seconds but if you pay attention to the same closely, you will notice that even though the final outcome is the same, the route taken to solve the Rubik’s cube is not the same. Every expert will start at a different place and will try to resolve it with different methods. Some will solve one color first and others will solve another color first. Even though they follow the same kind of algorithm to solve the puzzle they will start and end at a different place and their moves will be different at many occasions. It is  nearly impossible to have a exact same route taken by two experts. Big Market and Multiple Solutions Big Data is exactly like a Rubik’s cube – even though the goal of every organization and expert is same to get maximum out of the data, the route and the starting point are different for each organization and expert. As organizations are evaluating and architecting big data solutions they are also learning the ways and opportunities which are related to Big Data. There is not a single solution to big data as well there is not a single vendor which can claim to know all about Big Data. Honestly, Big Data is too big a concept and there are many players – different architectures, different vendors and different technology. What is Next? In this 31 days series we will be exploring many essential topics related to big data. I do not claim that you will be master of the subject after 31 days but I claim that I will be covering following topics in easy to understand language. Architecture of Big Data Big Data a Management and Implementation Different Technologies – Hadoop, Mapreduce Real World Conversations Best Practices Tomorrow In tomorrow’s blog post we will try to answer one of the very essential questions – What is Big Data? Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Reading data from an Entity Framework data model through a WCF Data Service

    - by nikolaosk
    This is going to be the fourth post of a series of posts regarding ASP.Net and the Entity Framework and how we can use Entity Framework to access our datastore. You can find the first one here , the second one here and the third one here . I have a post regarding ASP.Net and EntityDataSource. You can read it here .I have 3 more posts on Profiling Entity Framework applications. You can have a look at them here , here and here . Microsoft with .Net 3.0 Framework, introduced WCF. WCF is Microsoft's...(read more)

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