What is a Data Warehouse?

Data warehouse, also known as DWH is a system that is used for reporting and data analysis. It is considered to be the core of business intelligence (BI) as all the analytical sources revolve around the data Warehouse.

Data Warehouse Tools

A data warehouse tool is a key component in Big Data and data analytics. A data warehouse is an intelligent data repository that feeds analytics software, allowing users to data mine for competitive insight.

Data Warehouse Tools play an absolutely critical role in managing today’s data analytics process in businesses across all sectors. These tools work with an array of technologies, including DBMS (Database management system), DMA (data management for analytics) and DMSA (Data Management System and Analytics).

Increasingly, data warehouse tools use artificial intelligence and machine learning to boost performance. Today’s enterprise-grade CPD (cloud data platform) is a complex technology that combines structured and unstructured data into formats that are useful for analytics.

A list of the best open source and commercial Data Warehousing Tools and Techniques:

List of Best Data Warehouse Tools

• Amazon Redshift
• Microsoft Azure
• Teradata
• Google BigQuery
• Xplenty
• Informatica
• IBM Infosphere
• Oracle
• SAP
• Snowflake

Amazon Redshift

Amazon Redshift is an excellent data warehouse product which is a very critical part of Amazon Web Services – a very famous cloud computing platform.

Redshift is a fast, well-managed data warehouse that analyses data using the existing standard SQL and BI tools. It is a simple and cost-effective tool that allows running complex analytical queries using smart features of query optimization.

It handles analytics workload pertaining to big data sets by utilizing columnar storage on high-performance disks and massively parallel processing concepts.

One of its very powerful features is the Redshift spectrum, that allows the user to run queries against unstructured data directly in Amazon S3. It eliminates the need for loading and transformation. It automatically scales query computing capacity depending on data. Hence the queries run fast.

Microsoft Azure

Azure SQL data warehouse is a cloud-based relational database from Microsoft. You can optimize it for petabyte-scale data loading/processing and real-time reporting. The platform has a node-based system, and it employs massively parallel processing (MPP). The architecture is suitable for optimizing queries for concurrent processing. Thus, it enables you to extract and visualize business insights much faster.

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The data warehouse is compatible with hundreds of MS Azure resources. For example, you may build intelligent apps with the platform’s machine learning tools. Also, the platform lets you store different types of structured and unstructured data. The data may come from diverse sources, such as on-premise SQL databases and IoT devices.

Teradata

Teradata is a data warehousing platform for collecting and analyzing vast amounts of enterprise data in the cloud. The tool provides super-fast parallel querying infrastructure. This way, it speeds up access to actionable insights. Teradata’s QueryGrid delivers best-fit engineering. It does this by deploying multiple analytic engines to deliver the right tool for the job.

It also employs smart in-memory processing to optimize database performance at no extra costs. Using SQL, the data warehouse connects to commercial and open-source analytical tools.   

Google BigQuery

BigQuery is a cost-effective data warehousing tool with built-in machine learning capabilities. You can integrate it with Cloud ML and TensorFlow to create powerful AI models. It can also execute queries on petabytes of data in seconds for real-time analytics.

This cloud-native data warehouse supports geospatial analytics. With it, you may analyze location-based data or discover new lines of business.

BigQuery can separate compute and storage. So, it enables you to scale processing and memory resources based on business needs. Separation lets you manage the availability, scalability, and cost of each resource.

Xplenty

Xplenty is a cloud-based data integration platform to create simple, visualized data pipelines to your data warehouse. It will bring all your data sources together. With Xplenty you will be able to centralize all your metrics and sales tools like your automations, CRM, customer support systems, etc.

Xplenty is an elastic and scalable platform for data integration. It can work with structured and unstructured data. It can integrate data with a variety of sources like SQL data stores, NoSQL databases, and cloud storage services.

Key Features:

• Xplenty can be integrated with a variety of sources like SQL data stores, NoSQL databases, and cloud storage services.
• It can work with relational databases such as Oracle, Microsoft SQL Server, Amazon RDS, etc.
• You will be able to connect with online analytical data stores such as AWS Redshift and Google BigQuery.

Informatica

Informatica is a well-established and reliable name in data warehousing these days and was launched in 1993. Informatica organization has its headquarters in California. It holds a very good portfolio in data integration, ETL, B2B data integration, virtualization of data and information lifecycle management.

Informatica power center constitutes of three main components:

• Client tools: Installed on developer machines.
• Power Centre repository: A place to store metadata for an application.
• Power center server: Server to perform data executions.

With a growing customer base, Informatica is continuously trying to leverage its data integration solutions. This tool has inbuilt powerful mapping templates to help in managing data in an efficient manner.

IBM Infosphere

IBM Infosphere is an excellent ETL tool which uses graphical notations to execute data integration activities.

It provides all the major building blocks of data integration & data warehousing along with data management and governance. The building foundation of this warehousing architecture is a Hybrid Data Warehouse (HDW) and Logical Data Warehouse (LDW).

Multiple data warehousing technologies are comprised of a hybrid data warehouse to ensure that the right workload is handled on the right platform. It helps in proactive decision making and streamlining the processes. It reduces cost and is a very effective tool in terms of business agility.

This tool helps in delivering intensive projects by providing reliability, scalability, and improved performance. It ensures the delivery of trusted information to the end-users.

Oracle

here’s no argument that Oracle is a dominant market leader in database tools, and this strength carries over into the closely related market for data warehouse tools. The vendor’s data management products are widely seen as highly capable and as sophisticated as anything on the market.

In short, for those large enterprises with a robust budget, Oracle is often the default choice – many Fortune 500 companies consider Oracle as standard infrastructure. In the cloud, Oracle has caught up from a slow start – very much so. In contrast to its early questioning of the cloud, Oracle has since invested a vast sum in becoming cloud proficient – and has succeed in this. The company’s Autonomous Data Warehouse, which is cloud-based, enables the typically lower overhead expected of cloud-based products.

Any organization using Oracle data warehouse tools will have a plethora of robust tools. This includes the Oracle Big Data Management System, and the well-regarded Oracle Exadata Database Machine. The company’s now extensive menu of cloud-based choices typically has a compliment that is available as an on-premise data tool. 

SAP

The German software giant SAP, founded in 1972, is one of the top legacy vendors, yet no one suggests it’s stuck in the past. First, even among leaders in the data warehouse tool market, SAP is a dominant player – arguably the dominant vendor.

As a foreword looking vendor, SAP incorporates machine learning and artificial intelligence functions within its flagship SAP HANA solution, a leading data warehouse tool. For instance, HANA can leverage algorithms from TensorFlow. HANA also features an in-memory database management system (DBMS).

Also impressive: SAP has built an extensive network of alliances with major cloud providers, including the leaders AWS, Azure and Google Cloud, as well as with aggressive growing players like Alibaba. In essence, SAP is a cloud company by default. Among its many cloud-based data warehouse solutions are the SAP Data Warehouse Cloud and the SAP Cloud Platform Big Data Services, which uses Hadoop.

Snowflake

Founded in 2014, Snowflake is the new hip contender in the data warehouse tools arena – but one whose product portfolio holds its own with more mature contenders. In essence it’s been able to survey the competitors and launch a platform that’s more contemporary. This new player is already considered a market leader, and is known for its reasonable pricing.

The company offers an automated, cloud-based platform. Its flagship solution is a fully managed data warehouse on leading clouds like AWS and Azure. Impressively, its system is set up based on separation of resources, enabling one element to respond and scale based on its own workload demands. The net effect is a robust ability to handle an ever-changing, heterogenous infrastructure. Some customer note that Snowflake allows them to service a greater variety of use cases and more total workloads.

For data warehouses, being ACID-compliant (atomicity, consistency, isolation, durability) means transactions are processed with fewer hiccups. In addition to offering ACID-compliant, Snowflake also supports a wide array of formats, from Parquet and Optimized Row Columnar. The company touts a handful of key partnerships to extends its product offering.

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Conclusion

There are several options that are available to companies in data warehouse tools. This, in turn, lays stress over the importance of proper analysis of the organizational requirements and needs before picking any tool.

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