BLENGOK

Pivotal eyes cloud, big data, and app development

Analytical DBMS: Pivotal Greenplum Database.
In-memory DBMS: Pivotal GemFire and SQLFire. Pivotal HD used in combination with GemFire XD and HAWQ for in-memory analysis on top of Hadoop.
Stream-analysis option: Pivotal is working a project aimed at integrating its GemFire (NoSQL) and SQLFire in-memory data grid capabilities with Pivotal Hadoop and Spring XD as a data-ingest mechanism to support scalable, streaming-data analysis.
Hadoop distribution: Pivotal HD.
Hardware/software systems: Pivotal Data Computing Appliance

Theres no shortage of ambition at Pivotal, an EMC spinoff that offers big-data infrastructure as well as an abstraction layer for cloud computing (based on Cloud Foundry) and an agile application development environment (based on SpringSource). Pivotals big-data and analytics capabilities blend the Pivotal HD Hadoop distribution with GemFire SQL Fire in-memory technology, the Greenplum database, and HAWQ (Hadoop With Query) SQL querying capabilities. It also has close ties and in-database integrations with SAS analytics.

The question with Pivotal is just how much energy, investment, and "oomph" it can bring to three bold fronts of next-generation computing: big data, cloud, and application development. Pivotals largest competitors -- IBM, Oracle, and Microsoft -- can rely on the revenue from well-established data-integration, data-quality, BI, and analytics software that Pivotal lacks. Competitors such as Cloudera, Hortonworks, and Teradata can focus exclusively on big-data analytics. Time will tell if Pivotals products and execution can keep up with its bold ambitions for big data as well as cloud integration and application development.

Teradata delivers unified big data architecture

Analytical DBMS: Teradata, Teradata Aster.
In-memory DBMS: Although not an in-memory DBMS, Teradata Intelligent Memory monitors queries and automatically moves the most-requested data to the fastest storage tiers available, with options including RAM, flash, SSD, and various speeds of conventional spinning discs.
Stream-analysis option: None.
Hadoop distribution: Resells and supports the Hortonworks Data Platform.
Hardware/software systems: Teradata and Teradata Aster are integrated software/hardware systems. Hadoop is supported with two Teradata appliance offerings as well as standardized Dell configurations.

Teradata entered the big-data era boasting the largest roster of petabyte-scale enterprise data warehouse (EDW) customers of any vendor. It took a couple of years for the company to accept that SQL could not satisfy all needs, but in 2011 it acquired Aster Data and in 2012 it partnered with Hortonworks to build out what it calls its Unified Data Architecture (UDA).

The Teradata DBMS is at the heart of the UDA, supporting EDWs and marts for production BI and analytical needs. Options include SQL and various in-database analytic options including extensive support for SAS. The company has kept this DBMS at the forefront of performance with hybrid row and columnar compression and an Intelligent Memory feature for fast querying from RAM as well as SSDs, flash, or various speeds of spinning disks.

Aster is the UDA data-discovery platform, a small, transient store for day-to-day exploration of structured and multi-structured (clickstream, social, or machine) data. Analysis options include SQL, SQL-MapReduce, and SQL-graph analysis. Hadoop is the option for high-scale, low-cost storage, and subsets of data from this store can be copied to Teradata Aster or drawn into Teradata using SQL-H, the companys SQL-on-Hadoop option.

Hadoop boosters such as Cloudera would argue that cost and scale advantages will lead customers to do more of their analyses, including SQL, graph, and, of course, MapReduce, on Hadoop. Teradata is counting on its SQL-friendly ways -- and the foreign nature of Hadoop tools and languages for many practitioners -- to keep structured-data analyses in Teradata and variable-data analyses in Teradata Aster. The more popular, capable, and easy to use that Hadoop becomes, the less compelling a separate data-discovery platform will be. Regardless, theres no doubt that the core Teradata DBMS will continue to be a cornerstone of data management for lots of big and performance-driven companies.

Microsoft is all in on big data

Analytical DBMS: SQL Server 2012 Parallel Data Warehouse (PDW).
In-memory DBMS: SQL Server 2014 In-Memory OLTP (option available with SQL Server 2014, set for release by second quarter of 2014).
Stream-processing technology: Microsoft StreamInsight.
Hadoop distribution: HDInsight/Windows Azure HDInsight Service (based on Hortonworks Data Platform).
Hardware/software systems: Dell Parallel Data Warehouse Appliance, HP Enterprise Parallel Data Warehouse Appliance.

Microsofts vision is to provide a complete information production process for data -- all data. It has embraced Hadoop for high-scale and unstructured data, partnering to build HDInsight based on the Hortonworks Data Platform. HDInsight is available as a service on Microsofts Azure Cloud or can be used on premises. You manage HDInsight with Microsoft Systems Center, virtualize its resources with Microsoft Virtual Machine Manager, and control access to its data with Active Directory. Drawing from the hundreds of terabytes or petabytes in Hadoop, Microsoft customers can then analyze boiled-down result sets in SQL Server Parallel Data Warehouse. PDW is a massively parallel processing DBMS that includes a PolyBase access layer for combining non-relational data from Hadoop and structured data from a PDW warehouse or mart.

Microsoft analyzes streaming data with Microsoft StreamInsight, and it handles high-volume machine-data and technical analysis with its High Performance Computing Cluster. No matter how complex or high-scale the challenge, Microsoft sees the analysis getting back to SQL Server Analysis Services, Information Integration Services, Reporting Services, Master Data Services, and the worlds most popular BI tool, Office Excel with PowerPivot for in-memory analysis and PowerView for data visualization.

Theres no doubt that Microsoft is amassing all the pieces, but its early days for HDInsight, and we still dont see many PDW deployments after three years in the market. Were watching to see whether the release of SQL Server 2014, expected in the first half of this year, and the fast growth of the Windows Azure cloud, with its public data market and HDInsight service, help elevate Microsofts name in enterprise big data.

IBM has the broadest data-management portfolio in the industry, hands down. In addition to offering all the platforms mentioned above, as well as mainframes, IBM has a bevy of data-integration, data-cleansing, and data-quality software options to help capture and clean data. It also has plenty of business intelligence and analytics offerings, including Cognos, SPSS, text- and unstructured-data mining options, and IBM-developed tools for Hadoop including Big Sheets and BigSQL. IBM is also building out its SaaS portfolio and cloud infrastructure, with the $2 billion SoftLayer acquisition being a tangible example of the cloud commitment.

Although IBM has plenty of products and services, its not a product-oriented provider of technology. IBM leads with its deep integration and consulting expertise in a consultative approach focused on building business-differentiating "solutions" that might incorporate multiple products. The upside is that its not a cookie-cutter, one-size-fits-all approach, but competitors say beware of open-ended commitments and steep, ongoing consulting fees. Those choosing IBM expect an effective strategic approach that leads to significant business results. Its up to you to make sure you get what you pay for.