Appliance

General Data Appliance / Complimentary Database Engine – Datatupia

May 10, 2011/0 Comments/in Data Warehouse Appliance, Posts /by Adam Getz


A completely unique data warehouse appliance is provided by Datatupia. Rather than providing a solution that provides an organization a complete data warehouse solution, Datatupia’s solution compliments existing data warehouse deployments within an organization.

Dataupia’s flagship product, Satori Server

Datatupia is a data warehouse appliance in the sense that it combines software and hardware components on a rack-mounted server hardware. Additionally, Satori Server includes an embedded copy of the Linux operating system, a custom database engine, an aggregation engine, built-in storage, and parallel processors. But in contrast to full data warehouse appliances that are purpose-built for data warehousing, Satori Server interoperates with existing database management systems rather than requiring their replacement.

The main feature of Satori Server that makes it unique amongst all data warehouse appliances is “omniversal transparency“. This feature shields applications and users from changes to the underlying technology and database platform by complementing Oracle, MS SQL Server, or DB2 databases and their dependent applications. Unlike other solutions that displace existing technologies, the Satori Server’s innovative design extends and enhances an existing information eco-system. Further, Datatupia’s Satori Server is usually added to an existing data warehouse infrastructure by augmenting an organization’s existing data management systems as a query accelerator or capacity extender.

Partial Data Warehouse Appliances (Software Appliances)

May 1, 2011/1 Comment/in Data Warehouse Appliance, Posts /by Adam Getz

Starting in 2006, a new wave of vendors emerged that focus on database management systems (DBMS) purpose-built for data warehousing that easily integrate with another vendor’s hardware. The products from these “partial-solution” data warehouse appliance vendors have been coined as “software appliances”. The software appliance has proved to be a good starting point for new data warehouse vendors. It allows them to focus on their strength, database software, and to partner with commodity hardware vendors and benefit from the hardware firms’ focus and resources.

The partial solution vendor is typically a software company that has developed a proprietary database and either partners with or is a subsidiary of one or more major hardware vendors. These software vendors generally focus on the development of an optimal database management software that is suitable for large data warehouse implementations, and commonly rely on other vendors to supply the most-appropriate hardware.

Software appliance vendors with products based upon the traditional row-based database model:

Kognitio – Offers a row-based “virtual” database called WX2 that does not include indexes or data partitions and runs on blade servers from IBM and hardware from Hewlett-Packard. Kognitio can be deployed to a customer in one of three ways: as a software-only license solution, as a fully configured data warehouse appliance, and on-demand via a Data Warehousing as a Service (DaaS) offering.

Software appliance vendors with products based upon the columnar database model in which data is stored in columns rather than rows:

ParAccel – The ParAccel Analytic Database is a proprietary column-based database that combines patent-pending innovations with a columnar, compressed, massively parallel, all-in-memory-capable architecture. The database is available as a packaged appliance on Sun hardware known as Ultra-Fast Data Warehouse powered by Sun and ParAccel.
Vertica – The Vertica Analytic Database is a grid-based, column-oriented database that runs on grids of commodity Linux-based servers. It features column-oriented storage organization, compression, hybrid storage organization, and a shared nothing architecture. In March 2011, Hewlett Packard (HP) completed an acquisition of Vertica Systems, Inc.
EXASOL – Offers a proprietary, massively parallel database management system called EXASolution. The product is column-based and makes extensive use of in-memory processing. Join indexes are automatically generated at run-time, as required. EXASolution is available either as a software only solution or as a pre-configured appliance running on commodity hardware such as Fujitsu Siemens, HP, Supermicro or Hitachi. EXASolution uses its own version of Linux which provides extended capabilities to support its clustered architecture.
Sybase IQ – Distinguishes itself from conventional databases with its column-oriented, grid-based architecture, patented data compression, and advanced query optimizer. Sybase IQ provides significantly faster query performance than traditional database environments which were designed primarily for processing transactions. The improvement over such solutions can be 10 to 1000 times greater, even as thousands of users and years worth of data are added to the environment. Announced in May 2008, Sybase Analytical Appliance combines pSeries hardware from IBM with the Sybase IQ database.

Software appliance vendors with products based upon a hybrid row and column database:

EMC Greenplum – Acquired in July 2010 by EMC, Greenplum has become the foundation of EMC’s Data Computing Division. Moreover, the EMC Greenplum database contains a shared-nothing, massively parallel processing (MPP) architecture that has been purpose-built for business intelligence and analytical processing.
Aster Data – Provides a database management system (DBMS) called Aster Data nCluster, which is a massively parallel (MPP) hybrid row and column database with an integrated analytics engine. Aster Data’s solution utilizes Aster Data’s patent-pending SQL-MapReduce to allow for parallel processing of data and applications and to deliver rich, analytic insights. Aster Data products can be deployed to a customer in one of three ways: Software-only, Cloud-based, and Complete-appliance called Aster Data MapReduce DW Appliance. In April 2011, Teradata completed its acquisition of Aster Data Systems, Inc.

Complete Data Warehouse Appliance Solutions

April 25, 2011/0 Comments/in Data Warehouse Appliance, Posts /by Adam Getz


Complete data warehouse appliances are purpose-built data warehouse solutions and systems that encompass a whole-technology stack including:

• Operating System (OS)

• Database Management System (DBMS)

• Server Hardware

• Storage Capabilities

Initially DW appliances were created with proprietary custom-built hardware and storage units. Netezza, Teradata, DATAllegro, & White Cross (now Kognito) were the first vendors to provide solutions in this manner. Subsequently data warehouse appliances evolved and started to utilize lower-cost, industry-standard non-proprietary hardware components.The movement from proprietary to commodity hardware has proven to bring down the cost of the data warehouse appliance as the commodity hardware can integrated at a lower cost of both developing and integrating proprietary hardware. Examples of commodity hardware typically include general-purpose servers from Dell, Hewlett Packard (HP), or IBM utilizing Intel processors and popular network and storage hardware from either Cisco, EMC, or Sun.

Introduced 2002, Netezza was the first vendor to offer a complete data warehouse appliance, so early definitions of appliance were based upon Netezza products. Subsequently, Netezza Performance Server still provides all of the software components of a data warehouse appliance, including the database, operating system, servers, and storage units. However in 2009, Netezza replaced its own proprietary hardware with IBM blade servers and storage units. Further in 2010, IBM completed a corporate acquisition of Netezza.

Similar to Netezza, DATAllegro was launched in 2005 with a complete solution involving proprietary hardware. Soon after DATAllegro replaced its own proprietary hardware with commodity server from Dell and storage units from EMC. In 3008, Microsoft acquired DATAllegro in 2008 and announced it will integrate DATAllegro’s massive parallel processing (MPP) architecture into its own MS SQL Server platform, which also runs on commonly-available hardware.

Additionally, both Kognitio and Teradata replaced their proprietary hardware within their appliances in a process similar to that of DATAllegro. Kognitio now offers a row-based, in-memory database database called WX2 that does not include indexes or data partitions and runs on blade servers from IBM and Hewlett-Packard. Teradata provides a proprietary database, a variety of common operating systems (Linux, Unix, and Windows), and a proprietary networking subsystem packaged along with commodity processors and storage units.

Announced at the 2008 Oracle OpenWorld conference in San Francisco, the Oracle Exadata Database Machine is a complete package of database software, operating system, servers, and storage. The product was initially assembled in collaboration between Oracle Corporation and Hewlett Packard where Oracle developed the database, operating system and storage software, while HP constructed the hardware. However, with Oracle’s acquisition of Sun Microsystems, Oracle announced the release of Exadata Version two with improved performance and usage of Sun Microsystems storage and operating systems technologies.

At the Sapphire conference in May, 2010 in Orlando, SAP announced the release of its new data warehouse appliance called HANA or High-Performance Analytic Appliance. SAP HANA is a combination of hardware, storage, operating system, management software, and in-memory data query engine that is characterized by data being held in RAM rather than being read from disks or flash storage.

Finally IBM bundles and integrates its own Infosphere Warehouse database software (formerly “DB2 Warehouse”) with its own servers and storage to deliver the IBM Infosphere Balanced Warehouse.

Data Warehouse Appliance: Oracle Exadata

April 18, 2011/0 Comments/in Data Warehouse Appliance, Posts /by Adam Getz

Announced by CEO Larry Ellison at the 2008 Oracle OpenWorld conference in San Francisco, Oracle Exadata Database Machine is a complete database appliance with support for both transactional (OLTP) and analytical (OLAP) database systems. Delivered as a complete package of database software, operating system, servers, and storage, the Oracle Exadata Database Machine is simple and fast to implement and ready for large-scale business applications.

The product was initially assembled in collaboration between Oracle Corporation and Hewlett Packard (HP) where Oracle developed the database, operating system and storage software, while HP constructed the hardware. However, with Oracle’s acquisition of Sun Microsystems, Oracle announced the release of Exadata Version two with improved performance and usage of Sun Microsystems storage and operating systems technologies. The main idea of Exadata is to make the storage database aware and push processing of queries down to the disks for optimal scanning and performance. Subsequently an Exadata machine can scan 1 TB of data in about 3.5 seconds by scanning several (or all) disks in parallel with Oracle’s Parallel Query technology.

Oracle Exadata Database Machine
Currently the Oracle Exadata Database Machine provides a solution for all types of database systems, ranging from scan-intensive data warehouse applications to highly concurrent transactional applications. With its bundled combination of storage, database software, operating system, and standard hardware components from Sun, the Oracle Exadata Database Machine provides extreme performance within a highly-available, highly-secure environment. Additionally Oracle’s unique clustering and workload management capabilities position the Oracle Exadata Database Machine to be well-suited for consolidating multiple databases onto a single and centralized environment.

Facts and Benefits of Oracle Exadata Database Machine

• Accelerates data warehouse query performance by at least a factor of 10x.
• Runs more queries concurrently for faster access to business-critical information.
• Scales to 10x more concurrent users.
• Provides a trusted highly-available and cost-effective platform.
• Replaces and consolidates isolated special-purpose databases into one platform.
• Allows for massive parallel processing of data with a high-bandwidth.
• Easily expands with the connection of multiple units.
• Includes combination of Oracle Exadata Storage server, Oracle database software, Sun Solaris operating system (OS), and the latest industry standard hardware components from Sun.

Data Warehouse Appliance: SAP HANA

April 13, 2011/0 Comments/in Data Warehouse Appliance, Posts /by Adam Getz

At the Sapphire conference in May, 2010, SAP announced the release of its new data warehouse appliance called HANA or High-Performance Analytic Appliance. SAP HANA is a combination of hardware, storage, operating system, management software, and in-memory data query engine that is characterized by data being held in RAM rather than being read from disks or flash storage. Additionally, HANA has been built to split up queries to run in parallel on multiple processors—a fundamentally different architecture from SAP’s existing applications. This in-memory and parallel processing architecture of HANA allows for extremely fast performance of queries and analytics on very large amounts of data.

The SAP HANA solution has been introduced on Hewlett Packard x86 servers (HP ProLiant DL580 G7 and DL980 G7 servers) and is built upon Intel’s multi-core servers. Moreover, a single server blade can contain up to 2TB of main memory (4TB coming soon) and up to 64 processor cores. With this total solution, SAP claims that they beat the current performance benchmark by factor of 20, on hardware that was several dozens of times cheaper for a 200X price performance improvement. SAP also claims that HANA either reduces or out-right eliminates the need for the development and deployments of complex and expensive datamarts.

SAP intends HANA systems to be well-integrated with its own enterprise resource planning (ERP) systems, allowing for transactional data in SAP ERP systems to be analyzed in real time. However, HANA is not dependent solely on SAP ERP systems as a data source. Moreover, HANA is data source “agnostic” which means most common data sources and database can be integrated with it.

According to an SAP document, the HANA platform includes a modeling environment that is simple enough for business users to work with. Additional, HANA supports client interfaces currently include Microsoft Excel and SAP’s Business Objects business intelligence software.

References: SAP’s Transformation: A Work-In-Progress – Part One (ChainLink Research), SAP Launches HANA for In-memory Analytics (PC World)

Basic Architecture of a Data Warehouse Appliance

February 9, 2011/0 Comments/in Data Warehousing, Posts /by Adam Getz

By definition, a data warehouse appliance is a complete hardware and software solution that contains a fully integrated stack of processors, memory, storage, operating system, and database management software. The data warehouse appliance is typically constructed to be optimized for enterprise data warehouses, designed to handle massive amounts of data and queries, and designed to scale and grow over time. At its core, a data warehouse appliance simplifies the deployment, scaling, and management of the database and storage infrastructure as it provides a self-managing, self-tuning, plug-and-play database management system that can be scaled out in a modular manner.

Basic Architecture of a Data Warehouse (DW) Appliance

Fundamentally, a data warehouse appliance is a fully-integrated solution that contains …: – Database Management System (DMBS); – Server Hardware; – Storage Capabilities; – Operating System (OS)

The primary factor for the platform scalability and large-query optimization of the data warehouse appliance is its massively parallel processing (MPP) architecture. These MPP architectures are comprised of numerous independent processors or servers that all execute in parallel. Also known as a “shared nothing architecture”, the MPP appliance architecture is characterized by a concept in which every embedded server is self-sufficient and controls its own memory and disk operations. Further, the MPP architecture effectively distributes data amongst a number of dedicated disk storage units connected to each server in the appliance. Computations are moved as close to the data as possible and data is logically distributed amongst numerous system nodes.

Almost all large operations in the data warehouse appliance environment, including data loads, query processes, backups, restorations, and indexing are executed completely in parallel. This divide-and-conquer approach allows for extreme high performance and allows for systems to scale linearly as new processors can easily be added into the environment.