8 Most widely used Data Warehouse Tools

A Data Warehouse is a database that is designed to store large volumes of heterogeneous data. The data in a Data Warehouse is contributed from many departments, such as finance, customer service, marketing, sales, etc. It is gathered in a single centralized location and allows a business to organize and process data so that it can be analyzed easily. Data Warehousing revolves basically around 3 main processes i.e. Extract, Transform, and Load. This is also called an ETL process. This approach extracts relevant data from the source system. After extraction, the data quality is adjusted and improved to ensure that it is suitable for usage in a Data Warehouse. Finally, the data is loaded and is ready to be observed, evaluated, and analyzed to improve the product. A strategy to get the most out of your Data Warehouse is to leverage the correct Data Warehousing Tool. Data Warehouse Tools increase user productivity, be it from the early stages of a Data Warehousing design or a Data Analysis within an operational Cloud-based Data Warehouse

A Data Warehouse is a storehouse for information gathered from one or more sources. An E-Commerce business, for example, can utilize a Data Warehouse to integrate and aggregate consumer data. The role of a Data Warehouse is to streamline data for Business Intelligence. The ETL workflow in the Data Warehouse, on the other hand, is critical for the seamless transit of data from one architectural tier to the next.

Modern Data Warehousing systems, automate the repetitious operations of creating, developing, and implementing a Data Warehouse architecture to suit rapidly changing business requirements. As a result, many businesses use Data Warehouse Tools to gather detailed insights.

So you can see that how Data Warehousing has become so critical for large and medium-sized businesses. Apart from combining data from many sources, Data Warehouse makes it easy for the team to access data and gain insights from the information. Hence, Data Warehouse Tools are leveraged by businesses for the following purposes:

• To gain strategic and operational knowledge
• Accelerate decision-making and support systems.
• Examine and assess the effectiveness of marketing efforts.
• Analyze the performance of your employees.
• Keep an eye on consumer trends and forecast the next business cycle.

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. A data warehouse typically sits between large data storage repositories (like databases) and data marts. Data warehousing software, often used with ETL tools, enable reporting and analytics of all kinds, from business intelligence to predictive analytics.

Data warehouse tools collect, clean and organize large amounts of data from different sources for analysis and business insight. Data warehouse software plays a critical role in managing today’s data analytics process in businesses. 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).

Finding the right Data Warehouse Tool for managing and maintaining the Data Warehouse, as well as one that properly suits the specified business goals and limits, can be difficult. As a result, to make your search easier, the following is the list of the top 10 Data Warehousing Tools that organizations can use to streamline their Data Warehousing workflows:

Hevo Data: Hevo allows you to replicate data in near real-time from 150+ sources to the destination of your choice including Snowflake, BigQuery, Redshift, Databricks, and Firebolt. Without writing a single line of code. Finding patterns and opportunities is easier when you don’t have to worry about maintaining the pipelines. So, with Hevo as your data pipeline platform, maintenance is one less thing to worry about. For the rare times things do go wrong, Hevo ensures zero data loss. To find the root cause of an issue, Hevo also lets you monitor your workflow so that you can address the issue before it derails the entire workflow. Add 24*7 customer support to the list, and you get a reliable tool that puts you at the wheel with greater visibility. Check Hevo’s in-depth documentation to learn more. If you don’t want SaaS tools with unclear pricing that burn a hole in your pocket, opt for a tool that offers a simple, transparent pricing model. Hevo has 3 usage-based pricing plans starting with a free tier, where you can ingest upto 1 million records.

Amazon Web Services: Amazon Redshift is a good fit for enterprises that need top-level advanced functionality, and have the budget for a top tool and have the in-house staff that can manage AWS’s complex menu of solutions. If sheer number of data warehouses in the cloud determined the market leader, than Amazon Redshift would likely be the top data warehouse tool. Furthermore, given AWS’s gigantic footprint in the cloud market – clearly it’s the top cloud company – the company has a extensive tool set to compliment Redshift’s functionality. These related data warehouse tools include Redshift Spectrum, which offers advanced serverless functionality that queries data in local storage as well as the voluminous Amazon S3. Also available is Amazon Elasticsearch, a cloud-based search engine, and Amazon Kinesis, a data analytics service. Also notable is AWS Glue, a metadata catalog service.

Google Data Warehouse Tools: Google is famed for its Data Management skills, given its dominating position as a search engine. Google’s  Data Warehouse Tools reflect its cutting-edge Data Management and Analytics capabilities. Google Data Warehouse Tools for building context-rich apps, incorporating machine intelligence, and turning data into actionable insights include:

• Google BigQuery: Google BigQuery, in particular, is renowned for its ability to handle a wide range of complex business use cases. Google BigQuery is a business-level, Cloud-based Data Warehousing solution. The platform is designed to save time by storing and querying large datasets, using super-fast SQL searches against multi-terabyte datasets in seconds, offering customers real-time data insights. 

• Google Cloud Data Fusion: Google Cloud Data Fusion is a solution for integrating data in the Cloud. It’s a Google Cloud ETL solution that’s completely managed and allows data integration at any size. It has a visual point-and-click interface that allows you to deploy your ETL/ELT data pipelines without writing any code. It also includes 150+ pre-configured integrations and transformations at no additional cost, in addition to native interaction with Google Cloud Services.

• Dataflow: Dataflow is a Cloud-based data processing service that may be used to stream data in batches or in real-time. Developers can use it to build processing pipelines for integrating, preparing, and analyzing large data collections.

• Cloud Dataprep: Cloud Dataprep is a Cloud-based data exploration, cleaning, and preparation service for structured and unstructured data. Since Dataprep is serverless and scales to any size, no infrastructure is required to deploy or administer it.

• Google Data Studio: Google Data Studio is a Business Intelligence application that allows you to turn your data into entirely customizable, easy-to-read reports and dashboards that you can share. The Google Data Studio BigQuery connection allows you to access data from BigQuery tables using Google Data Studio.

Oracle: Oracle LogoThere’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.

Microsoft Azure Data Warehouse Tools: Microsoft Azure is a Cloud computing platform that was introduced in 2010. It allows developers to create, test, deploy, and manage applications and services using Microsoft-managed data centers. Azure is a public Cloud computing platform that provides Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). It offers 200+ products and Cloud services.

• Azure SQL Database: For Data Warehousing applications with up to 8 TB of data volumes and a significant number of active users, Azure SQL Database is a suitable choice. It is a fully managed Platform as a Service (PaaS) database engine that takes care of most database maintenance tasks including updating, patching, backups, and monitoring. Azure SQL Database is built on the Microsoft SQL Server database engine’s most recent stable version. Advanced query processing capabilities, such as high-performance in-memory technology and intelligent query processing, are also supported by it.

• Azure Synapse Analytics: Data Integration, Big Data Analytics, and Enterprise Data Warehousing are all part of Microsoft Azure Synapse Analytics. It employs Machine Learning technologies for applications and extracts significant insights from any data. By delivering an end-to-end Analytics solution, Azure speeds up project development. The data is entirely protected using the most up-to-date privacy and security technologies available in the market. 

• 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.

IBM: With a vast install base and a variety of Data Warehouse and Data Management solutions, IBM is a preferred choice for large business clients. The firm is known for its vertical data models and in-database and real-time Analytics, which are particularly essential in Data Warehousing. Following are some of the widely employed IBM Data Warehouse Tools:

• IBM Db2 Warehouse: IBM Db2 Warehouse is a Cloud Data Warehouse that enables self-scaling data storage and processing. It contains the Db2 relational database and enables you to store, analyze, and retrieve data as quickly as possible. It allows for automatic scaling and deployment flexibility. With Spark and R open-source, predictive modeling techniques are integrated directly into the database, making enterprise AI quicker and more efficient. With only a few clicks, you can transform unstructured data sources into a structured format for analysis.

• IBM Datastage: IBM Datastage takes data from a source system, transforms it, and feeds it into a target system. It allows customers to combine data from several corporate systems using an On-Premises or Cloud-based parallel architecture. You can leverage data lineage as well as prebuilt connections and stages to understand how data moves through transformation and integration.

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.

Conclusion

In this article, we gained a basic understanding of Data Warehouse and working with Data Warehouse Tools. We understood the need for the Data Warehouse Tools in your use case. Moreover, you learned some of the important factors that you need to keep in mind while selecting the right DataWarehousing Tool. Furthermore, this article provided a comprehensive overview of 8 most popular Data Warehouse Tools used in the industry.