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Data Warehouse Tutorial
  • Basic
    • What is Data Warehouse
    • Data Warehouse tools
    • Career in Data Warehousing
    • Benefits of Data Warehouse
    • Data Warehouse Architecture
    • Data Warehouse Design
    • Data Warehouse Implementation
    • Data Warehouse Features
    • Data Warehouse Modeling
    • Data Warehouse Software
    • Data Warehousing
    • Types of Data Warehouse
    • 10 Popular Data Warehouse Tools
    • Data Lake Architecture
    • Three Tier Data Warehouse Architecture
    • Data Warehouse Process
    • Database Parallelism
    • What is OLTP
    • What is OLAP
    • OLAP Tools
    • Types of OLAP
    • Operations in OLAP
    • MOLAP
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    • Data Warehouse Schema
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    • What is Star Schema
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    • What is Fact Table
    • Kimball Methodology
    • Data Warehouse Testing
    • Operational Data Stores
  • ETL
    • What is Data Mart
    • What is Data Cube
    • What is a Data Lake
    • What is Data Integration
    • What is ETL
    • What is ETL Testing
    • ETL Testing Tools
    • ETL architecture
    • Dimension Table
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    • Fact Constellation Schema
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  • Interview Questions
    • Data Warehouse Interview Questions
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10 Popular Data Warehouse Tools

By Swati TawdeSwati Tawde

Overview Data Warehouse Tools

In the world of computing, a data warehouse is defined as a system that is used for data analysis and reporting. Also known as an enterprise data warehouse, this system combines methodologies, a user management system, a data manipulation system, and technologies for generating insights about the company. The data warehouse stores current and historical data as repositories of data from multiple sources. They are then used to create analytical reports that can either be annual or quarterly.

data warehouse

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Companies then use these reports to make detailed sales analyses and marketing campaigns that can effectively take them to the next growth stage. Before the data is used for data warehouse reporting, it may also be used for operational data storage. Many big companies use separate warehouses to collect and maintain data effectively.

How did the data warehouse originate?

Data warehousing dates back to the late 1980s when Barry Devlin and Paul Murphy from IBM developed a business data warehouse. It was developed to provide an architectural model for data flow, specifically from operational systems to decision support environments. By addressing problems related to the flow, it tried to support multiple environments effectively. Thus by introducing the concept of a data warehouse, Bill and Ralph were considered the pioneers of the data warehouse. This means that before the concept of a data warehouse, data storage and synchronization were not conducted. Post the development of business data warehouses, it has come a long way and is today an integral part of companies and economies worldwide.

data warehousing

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Features of data warehousing

Some important features are as given below.

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It provides companies with comprehensive decision-making support

As the core components of any company involve making plans and developing methodologies and techniques to achieve organizational goals, a data warehouse can support great support to help them to do this. This is because data that is conceptualized and compiled properly can go a long way in helping companies to strategies and create long-term plans.

It helps in subject orientation.

An important feature of this is that it is oriented towards the subject. As data is gathered from numerous sources, it helps companies to use specific data that applies to their field. This helps a company gain insight into how data can be used so that all the company’s sectors are properly benefited. In addition, helping companies handle specific areas like management or IT can help them grow strategically and comprehensively.

It helps to integrate data.

After data complies from different sources, it allows for data integration. This means that data is dynamic and applicable to various departments. Therefore, data integration is one of the most important features of the data warehouse.

It allows for flexibility in time.

As data is stored strategically, data has a specific time duration. This makes it easier for companies to access data for a particular period. It is always better to have data structured in a time-specific manner because it can help companies to find loopholes in management and overall functioning on the one hand and make an effective comparison on the other hand.

It keeps data safe and secure.

Before the development of the data warehouse, secondary storage was considered the best way to save data. However, data warehouse supports integration, cohesiveness, and multi-application of data, making them a more suitable choice. This is because it helps to preserve data for future use as well. Moreover, as data in a warehouse is secure, it is one of the effective methods to store data for future use.

It allows companies to store large volumes of data.

Today the data available to companies is almost limitless. And data warehouse is more than capable of meeting this challenge as the warehouse size can be increased depending on the data. In addition, different organizations have different amounts of data they want to save for future use. Hence, a data warehouse is one perfect way to meet that requirement effectively.

It is accurate and grounded.

Data in a data warehouse is accurate and grounded, containing all techniques and theories. Many companies depend on data insights to make future decisions, which is an extremely important feature. If data is incorrect, it can affect the company’s progress and growth. As many technologies protect data in the warehouse, companies can be assured that their data is effective, discrete, and multi-dimensional.

It is the future of all companies, be it big or small.

Since it was officially introduced in 2002, it has steadily grown in popularity and has become an integral part of many companies and brands. As many companies use a data warehouse to preserve and gain insights about data, there are many advancements in this field by engineers that are making data warehouses more progressive and advanced. A data warehouse is one of the most effective techniques to save large amounts of dynamic data; a data warehouse is something that all companies must consider to reach the next stage of growth and development.

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What are some of the popular data warehouse tools available?

Therefore, every company must look at these tools going into the future. Here are some of the most popular data warehouse tools to help your company meet its growing and comprehensive needs.

  1. Ab Initio Software

Developed by Ab Initio Software, the products produced by this company are aimed at helping companies to perform functions related to fourth-generation data analysis, batch processing, data manipulation, and graphical user interface (GUI) based parallel processing software. (GUI-based software is commonly used to extract, transform and load data.) Ab Initio Software is a company that specializes in producing high-volume data processing applications and was founded more than 20 years ago, giving them considerable expertise in this field. Some of the products manufactured by the company include Graphical Development Environment, Co-operating System, and Enterprise Meta, among others. Further, the company also introduced a free feature-limited version known as Elementum in 2010, though it was only available to customers who have a commercial license from the company.

  1. Amazon Redshift

Another hosted data warehouse product, Amazon Redshift, is a part of Amazon Web Services, a large cloud computing platform. Based on technology from the massive parallel processing, Redshift is different from other databases offered by Amazon. This is because Amazon Redshift can handle analytics workloads of large quantities. To handle such huge data, the company uses massively parallel processing. Amazon Redshift partners that provide data integration tools include Alooma, Attunity, FlyData, Informatics, SnapLogic, Talend, and Xplenty.

  1. AnalytiX DS

A software vendor, AnalytiX DS provides specialized data mapping and tools for data integration, data management, enterprise application integration, and big data software and services. With its main office in Virginia, the company has offices in Asia and North America with an international team of service partners and technical assistants. The founder of AnalytixX DS, Mike Boggs, was responsible for coining the term pre-ETL Mapping. Further, the company launched AnalytiX Mapping manager, a premier tool capable of automating pre_ETL source to the target mapping process. With an investment of 50-100 crore, AnalytiX Ds might soon open a new development center in Bangalore in the coming years.

  1. CodeFutures

Founded in 2001 by Andy Grove, CodeFutures is based in the United States. The main software of this company is called DB shard, a NewSQL platform based on database sharing. What sets this apart from other SQL products is that DB shard has been designed to provide scalability to companies and can be used with traditional database platforms like MySQL and PostgreSQL. As a result, companies will not have to replace their existing database engine, but DB shards can be used along with them.

  1. DATAllegro

Another database warehouse tool, DATAllegro, provides companies with appliances that perform various data warehouse functions. Founded by Stuart Frost in 2003, it was a direct competition to the data warehouse appliance created by Netezza. While Netezza used commodity PowerPC chips, DATAllegro was implemented on the commodity hardware. These included hardware on systems like Dell, CISCO, and EMC Corp. However, like Netezza, DATAllegro also used open source software stack. In 2008, Microsoft acquired the company, and the SQL Server Data Warehouse is a successor to DATAllegro, which uses a version of the SQL server database engine.

  1. Holistic Data Management

A framework that is AHISDATA, holistic data management is used for implementing software within a company network. The framework can perform various functions, including data governance, quality, integration, and master data management. Some Holistic Data management specifications are the following: 1. All data objects in the warehouse must either be a child data object or a parent data object 2. The data network scope must have only one parent data object Data mapping link must be present within all child data objects 4. In the data management modules, there must exist at least one data object relationship

  1. Informatica Corporation

A software development company, Informatics, was founded in 1993 in California. A product portfolio focuses on data integration, cloud data integration, B2B data exchange, ETL, Information lifecycle management, data replication, data virtualization, complex event processing, and other functions. Together, these components provide data warehouse facilities to companies across sectors. The informatics Power center has three main components: Informatica Power center client tools (installed at the developer end), Informatics Power center repository (where all the metadata for an application is stored.) Informatica Power center server (where all the data executions occur.) With a customer base of over 5000 companies, Informatics has also launched Informatica Marketplace to allow the company to stop sharing and leverage data integration solutions. With a host of features, this tool has over 1300 pre-built mapping, templates, and connectors to help companies manage and empower their data effectively.

  1. ParAccel

A California-based software company, ParAccel provides a database management system for companies and organizations across all sectors. The company was acquired in 2013 by Actian. Two of the products offered by ParAccel are Amigo and Maverick. Amigo has been designed to speed up the process of queries generally directed toward the existing data warehouse. Maverick has been designed to be a stand-alone data store for companies. ParAccel scrapped Amigo in favor of Maverick, which later evolved into the ParAccel Analytic Database. A parallel relational database system, the ParAccel Analytical Database uses a shared-nothing architecture with columnar orientation and memory-centric design to provide data analysis comprehensively. In addition, ParAccel also offers built-in analytic functions like standard deviation and two-shelf Analytics packages called Base package and Advanced Package.

  1. Teradata Corporation

A publicly held international company with its headquarters in Ohio, Teradata offers analytic data platforms and related services to different companies. The analytic products of Teradata are supposed to help companies to consolidate data from numerous sources and help them infer unique and important insights from them. It has two divisions: data analytics and marketing applications, which look after data analytics platforms and marketing software. Teradata allows companies to recall and analyze data simply and effectively by providing a parallel processing system. One of the most important features of this data warehouse application is that it segregates data into hot and cold, whereas cold data is that which is not frequently used. Further, Teradata is considered one of the most popular database warehouse applications.

Scriptella: An open-source ETL and script execution tool, Scriptella is written in Java. It allows the use of SQL or another scripting language for the data source. However, does not offer any graphical user interface. In addition, Scriptella is used for database migration, creation/update scripts, cross-database ETL operations, and import/export, among other functions.

Overall the number of database warehouse tools available to companies is many. That is why companies need to access their requirements and figure out which data warehouse tool can effectively help them grow and empower their growth story strategically and successfully.

Recommended Articles

This has been a guide to Data Warehouse Tools. Here we have discussed a brief overview of some popular tools with features. You may also have a look at the following articles to learn more –

  1. Data Warehouse Software
  2. Data Warehouse Process
  3. Data Warehouse Testing
  4. Data Warehouse Design
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