Business Intelligence: Microsoft Releases for the BI Technology Stack (Timeline)

Business Intelligence Series

I started some years back to put together a timeline for the most important events happening in the BI technology stack (work in progress):

2023: Microsoft announces Microsoft Fabric (>>)

• Synapse Data Warehouse is the next generation of data warehousing in Microsoft Fabric with native support for the delta lake.
• Data Engineering & Data Science workloads with support for lakehouses, notebooks, Spark Job definitions, models and experiments.
• Real-Time Analytics is a robust platform tailored to deliver real-time data insights and observability analytics capabilities for a wide range of data types.
• OneLake provides a single unified storage location for all your data analytics needs.

2022: Microsoft releases SQL Server 2022 (>>)

• Synapse Link for SQL Server 2022 allows to seamlessly replicate operational data in near real-time to be able to have more powerful analytics.
• Purview is a unified data governance and management service.

2019: Microsoft launches Azure Synapse Analytics service (formerly SQL Data Warehouse), a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics. (>>)

2019: Microsoft releases SQL Server 2019 (>>)

•  Big Data Clusters add-in for SQL Server allows to deploy scalable clusters of SQL Server, Spark, and HDFS containers running on Kubernetes (feature to be retired)

2018: Microsoft extends PowerQuery with ETL capabilities. (>>)

2018: Microsoft releases Azure Data Studio, a data management tool that enables to work with SQL Server, Azure SQL DB and SQL DW from Windows, macOS and Linux. (>>)

2017: Microsoft releases Power BI Report Server, an on-premises server that enables Power BI Pro users to publish Power BI reports and distribute them broadly across the enterprise, without requiring report consumers to be licensed individually per use (>>)

2017: Microsoft released SQL Server Data Tools (SSDT), which uses PowerQuery to import and prepare data in SSAS/AAS tabular models.

2017: Microsoft releases SQL Server 2017. (>>)

• SSRS is no longer available to install through SQL Server setup.
• Python support added, R Services renamed to Machine Learning Services. (>>)

2016: Microsoft releases SQL Server 2016 (What's new, >>)

• Query Store allows to monitor and troubleshoot performance issues.
• SQL Server R Services integrate the R programming language into SQL Server.
• Direct Query for SSAS.
• PolyBase for querying the data stored in HDFS. (>>)
• Support for Support for HDFS in SSIS.
• Azure SQL Data Warehouse is GA. (>>)
• modern reports with SSRS. (>>)
• Real-Time Operational Analytics. (>>)

2016: SQL Server 2014 Developer Edition becomes free. (>>)

2015: Microsoft announces elastic databases SQL Data Warehouse & Azure Data Lake. (>>)

• Elastic databases allows to build SaaS applications to manage large numbers of databases that have unpredictable resource demand.
•  Azure SQL Data Warehouse is an elastic data warehouse in the cloud that can dynamically grow, shrink and pause compute in seconds independent of storage.
• Azure Data Lake is a hyper-scale data store for big data analytic workloads.

2015: Microsoft releases Power BI to the general public.

• Power BI Designer renamed to Power BI Desktop.

2015: Microsoft releases several Azure services:

• launches the SQL Server Cloud database.
• Azure Data Factory (ADF), a fully managed service that does information production by orchestrating data with processing services as managed data pipelines. (>>)
• Azure Stream Analytics, a fully managed stream processing engine that is designed to analyze and process large volumes of streaming data with sub-millisecond latencies. (>>)

2014: Microsoft released Power BI Designer unifying Power Query, Power Pivot & Power View.

2013: Microsoft announces Power BI for Office 365. (>>)

2012: Microsoft releases with SQL Server 2012. (>>)

• BI Semantic Model for SSAS provides a single, scalable model for BI applications.
• Parallel Data Warehouse with PolyBase capabilities. 
• in-memory capabilities. (>>)
• Windows Azure SQL Reporting service available (>>)
• SQL Server Data Tools unifies SQL Server and cloud SQL Azure development for both professional database and application developers.

2010: Microsoft released 

• Power Pivot as part of SQL Server R2.
• Azure SQL Database.

2010: Microsoft releases SQL Server 2008 R2.

• Master Data Services.
• Power Pivot & Self-service BI capabilities in SSAS.

2008: Microsoft releases SQL Server 2008 (>>)

• Table compression.
• Change Data Capture (CDC).

2005: Microsoft releases SQL Server 2005

• a greatly enhanced version of Analysis Services.
• SQL Server Integration Services to replace DTS.

2004: Microsoft released SQL Server Reporting Services (SSRS) as add-on to SQL Server 2000.

2000: Microsoft released SQL Server Analysis Services (SSAS) with SQL Server 2000.

1998: Microsoft released SQL Server 7.

• OLAP services & first MDX specifications.
• Data Transformation Services (DTS) for ETL workloads.

SQL Server Administration: System.OutOfMemoryException in SQL Server Management Studio and other 32-bit Drawbacks

    I was playing this week with a few datasets downloaded from the web on various topics, trying to torture the data until they’ll confess something. A few of the datasets were prepared for load into a MySQL database as individual INSERT INTO statements. They were containing between 100000 and a few millions of records. While looking at the big but slim datasets in SSMS (SQL Server Management Studio) and reconciling the differences between MySQL and SQL Server I got several times the System.OutOfMemoryException exception, SSMS crashing one or two times. That should be ok, given the number of records, though I was surprised that I got the same error message while executing the INSERT INTO statements for one of the smallest datasets which had about 300000 records:

    „An error occurred while executing batch. Error message is: Exception of type 'System.OutOfMemoryException' was thrown”

    Kb 2874903 brings some light into the topic – SSMS is still a 32-bit process and thus limited to 2GB of memory. The Kb offers three methods to avoid this issue. The first two, outputting the query results to text or file didn’t worked. The third method based on using sqlcmd utility worked smoothly with a syntax like the one below:
sqlcmd -i “<file_name.sql>” -d “<database name>”

    So it doesn’t matter that you’re having a supercomputer and that working with big datasets becomes a necessity nowadays, this limitation can make data loading just a little bit more complicated. On one side, it’s true that when dealing with such datasets is probably recommended to use directly sqlcmd to execute the scripts. On the other side, independently from this type of problem, even if understandable from the need of keeping backwards compatibility with 32-bit platforms/solutions, it’s hard to digest the fact that Microsoft keeps some of its products 32-bit based when SQL Server is targeting 64-bit platforms. One has same problem when using BIDS (Business Intelligence Development Studio), developing SSRS, SSIS or SSAS solutions under 32-bit and having maybe to deploy the code as 64-bit (e.g. SQL Server Agent). From my point of view most of the issues I had were when dealing with proprietary drivers like the ones for Oracle or even for MS Office. In addition in SSIS there could be features that are only available in 32-bit versions, or have limitations on 64-bit computers (see [5]). As it seems also the SQL Server Data Tools (SSDT) will have similar drawbacks…

   Anyway, sqlcmd utility saved the day with a minimum of overhead. Unfortunately it’s not always that easy to solvethe compatibility issues between 32-bit and 64-bit software and platforms.

Update 20.06.2017:
   One can synchronize the runtime version between BIDS and SQL Server Agent pretty easy. In BIDS under "Configuration Properties/Debug Option" at Project level, there is the “Run64BitRuntime” Property. Set to false it will run your package on 32-bit version. In a SQL Server Agent Package, there is the “Use 32 bit runtime” Checkbox under “Execution options” at step level. Checking this checkbox will run your package on 32-bit version.

   A hint that the two values might be out of synch is the following error message raised when running the package:
"Attempt to load Oracle client libraries threw BadImageFormatException. This problem will occur when running in 64 bit mode with the 32 bit Oracle client components installed."
Resources:
[1] Microsoft Support (2013) Kb 2874903: "System.OutOfMemoryException" exception when you execute a query in SQL Server Management Studio https://support.microsoft.com/en-us/kb/2874903
[2] MSDN (2016) SQL Server 2016: sqlcmd Utility https://msdn.microsoft.com/en-us/library/ms162773.aspx
[3] MSDN (2016) SQL Server 2016: Use the sqlcmd Utility https://msdn.microsoft.com/en-us/library/ms180944.aspx
[4] MSDN (2012) Introducing Business Intelligence Development Studio  https://msdn.microsoft.com/en-us/library/ms173767.aspx
[5] SQL Server 2008 R2: 64 bit Considerations for Integration Services https://technet.microsoft.com/en-us/library/ms141766(v=sql.105).aspx

Data Warehousing: Cube Definitions)

"A subset of data, usually constructed from a data warehouse, that is organized and summarized into a multidimensional structure defined by a set of dimensions and measures. A cube's data is stored in one or more partitions." (Microsoft Corporation, "SQL Server 7.0 System Administration Training Kit", 1999)

"Name for a dimensional structure on a multidimensional or online analytical processing (OLAP) database platform, originally referring to the simple three-dimension case of product, market, and time." (Ralph Kimball & Margy Ross, "The Data Warehouse Toolkit" 2nd Ed, 2002)

"Proprietary data structure used to store data for an online analytical processing (OLAP) end user data access and analysis tool." (Sharon Allen & Evan Terry, "Beginning Relational Data Modeling" 2nd Ed., 2005)

"A multidimensional data structure that represents the intersections of each unique combination of dimensions. At each intersection there is a cell that contains a data value." (Reed Jacobsen & Stacia Misner, "Microsoft SQL Server 2005 Analysis Services Step by Step", 2006)

"Used with online analytical processing (OLAP), data cubes are multidimensional structures built from one or more tables in a relational database(s)." (Sara Morganand & Tobias Thernstrom , "MCITP Self-Paced Training Kit : Designing and Optimizing Data Access by Using Microsoft SQL Server 2005 - Exam 70-442", 2007)

"A multidimensional structure that contains dimensions and measures." (Robert D Schneider & Darril Gibson, "Microsoft SQL Server 2008 All-in-One Desk Reference For Dummies", 2008)

"A multidimensional structure that contains dimensions and measures. Cubes are a denormalized version of either the entire database or part of the database and are used within SQL Server Analysis Services (SSAS)." (Robert D. Schneider and Darril Gibson, "Microsoft SQL Server 2008 All-In-One Desk Reference For Dummies", 2008)

"A set of data that is organized and summarized into a multidimensional structure defined by a set of dimensions and measures." (Jim Joseph, "Microsoft SQL Server 2008 Reporting Services Unleashed", 2009)

"A database object that organizes data for accessibility in an OLAP database." (Ken Withee, "Microsoft® Business Intelligence For Dummies®", 2010)

"A multi-dimensional data structure that contains an aggregate value at each point, i.e., the result of applying an aggregate function to an underlying relation. Data cubes are used to implement OLAP." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Refers to the multidimensional data structure used to store and manipulate data in a multidimensional DBMS. The location of each data value in the data cube is based on the x-, y-, and z-axes of the cube. Data cubes are static (must be created before they are used), so they cannot be created by an ad hoc query." (Carlos Coronel et al, "Database Systems: Design, Implementation, and Management" 9th Ed, 2011)

"A set of data that is organized and summarized into a multidimensional structure that is defined by a set of dimensions and measures." (Microsoft, "SQL Server 2012 Glossary", 2012)

"A multidimensional representation of data needed for online analytical processing, multidimensional reporting, or multidimensional planning applications." (Sybase, "Open Server Server-Library/C Reference Manual", 2019)

"Cubes, also known as OLAP cubes, are preprocessed and presummarized collections of data that drastically improve query time. [...] OLAP cubes are logical structures as defined by the metadata." (Piethein Strengholt, "Data Management at Scale", 2020)