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Data warehousing is one of the most important factors in reporting and data analysis; it’s considered the core component of business intelligence. This article will discuss essential data warehouse tools.
Building a data warehouse was historically a complex process, requiring lots of infrastructure for data storage. But Cloud computing technology has amazingly reduced the effort and cost of data warehousing. Due to this reason, data warehousing is moving forward with fast, efficient, and scalable Cloud-based tools.
What Is a Data Warehouse?
A data warehouse is a data management system mainly designed to support data analytics and Business Intelligence (BI). It’s a repository of a large amount of data that’s collected from numerous sources. These data are stored under a unified schema and usually reside at a single site.
Data Warehouses are built with a process that includes several steps, such as data cleaning, integration, transformation, loading, and periodic data refreshing.
Typical data warehouse framework
What Are the Top Data Warehouse Tools?
There are different types of tools involved in data warehousing. We will discuss a few top data warehouse tools in this section.
1. Vertabelo
Data modeling is the first step of any data engineering project, such as building a data lake or warehouse. Having a good data model is essential for a successful project. This is where we need a high-quality data modeling program that supports data warehousing and works well with other tools.
The Vertabelo data modeler is a fabulous online solution that can work with other Cloud-based data warehousing tools. This data design tool supports the most popular database engines (like Amazon Redshift, Big Query, and Snowflake) used for data warehousing.
Vertabelo comes with many features, including more automated options that simplify data engineering tasks. Its clean and responsive user interface (UI) provides a fantastic working environment and offers the most popular notations (like Crow’s Foot, IDEF1X, and UML), allowing modelers to design all levels of data models (conceptual, logical, and physical) from scratch.
However, you don’t have to manually go through each stage and create all the levels of your data model. Vertabelo allows you to automatically generate a physical data model from its logical version. It also offers SQL generation, DDL import and reverse engineering, and more.
That’s not all. You can work collaboratively with your team using Vertabelo’s model sharing options. And its live model validation allows you to validate your models against your chosen database management system (DBMS).
Learn more about Vertabelo Features here.
2. Amazon Redshift
Source: Amazon
Amazon Redshift is one of today's most effective, scalable, and powerful Cloud-based data warehouse solutions. It’s a quick and easy-to-use relational database management system (RDBMS) that’s ideal for data warehousing
Redshift is compatible with other RDBMS applications as well as with Business Intelligence tools. BI solutions employing common ODBC and JDBC connections can quickly query structured data using Amazon Redshift's SQL-based clients.
With Amazon Redshift, you can analyze data from a number of sources in one place. You can quickly query hundreds of billions of rows using parallel SQL queries that simultaneously process data across all nodes. Redshift also manages your clusters automatically, so you don't have to worry about backup and recovery.
Learn more about designing a data model for Amazon Redshift.
3. Snowflake
Source: Snowflake
Snowflake is a Cloud-based data warehouse. It allows you to store, manage, and analyze all of your data. It is developed on top of Amazon Web Services or Microsoft Azure Cloud infrastructures. At any time, clusters can be automatically expanded and scaled in or out.
Because Snowflake supports well-known programming languages like JavaScript and Python, it is a very flexible tool for engineers. Learn more about Snowflake databases and data modeling.
4. Google BigQuery
Source: Google BigQuery
BigQuery, a serverless and cost-effective data warehouse, enables database specialists to perform scalable analyses on large amounts of data. This tool leverages built-in machine learning (ML) and AI/BI tools for broad insights. This enables data scientists and analysts to quickly develop and operationalize ML models on structured, semi-structured, and unstructured data using simple SQL.
Also, BigQuery Omni is a great (and cost-effective) multi-Cloud analytics solution. It provides features like real-time analytics with streaming data pipelines, and it can unify and manage all types of data.
Learn to build physical ER diagrams for Google BigQuery.
5. SAP HANA Cloud
Source: SAP
SAP HANA Cloud is a modern database as a service (DBaaS) offering. It supports next-generation applications and analytics across all types of enterprise data.
This is an all-in-one and flexible solution that can deliver transactions and analytics simultaneously without duplication. It can run high-performance transactional applications and provides easy real-time access to all your data.
Data scientists can use this solution to develop modern intelligent data applications, with embedded data science and machine learning (ML) enhancing business processes. SAP HANA Cloud supports multiple data types and efficiently manages data with integrated multi-tier storage.
Also, this solution offers trusted in-memory performance for all data requirements.
6. Hevo Data
Source: Hevo
Hevo Data is a complete data pipeline platform that makes sending operational intelligence to business tools simple. It helps you conduct analytics transformations on data from all your sources and draw data from all your sources into the warehouse.
This ETL (Extract, Transform, and Load) tool enables you to extract data in near real-time from various sources to the destination of your choice (such as Redshift, Snowflake, BigQuery, Databricks, and Firebolt) without writing a single line of code.
You can ensure zero data loss with Hevo Data. By monitoring your workflow with this tool, finding the reason for a problem is straightforward. It enables you to address the issue before it affects the entire workflow.
7. Talend
Source: Talend
Talend is a complete, flexible, and trusted modern data management solution that drives real value. Talend enables you to manage all stages of the data lifecycle, providing quality data at your fingertips. This data warehousing ETL tool provides data integration, data integrity, governance, API, and application integration.
Talend supports every Cloud services data warehouse and all the popular public Cloud infrastructure providers.
A few key features of Talend include providing a user interface to design flow and transformation logic and supporting most on-premises and Cloud databases. Also, Talend provides solutions for various industries like healthcare, telecommunications, and retail.
8. Cloudingo
Source: Cloudingo
Cloudingo makes managing your customer data easy. This data integrity and cleansing tool was designed for Salesforce and is popular among most data scientists.
It monitors and tackles every issue, from deduplication and data migration to recognizing human errors and data inconsistencies. Cloudingo manages data imports, offers a great degree of control and flexibility, and has robust security safeguards.
Also, it eliminates coding and spreadsheets using a drag-and-drop graphical interface.
9. IBM InfoSphere QualityStage
Source: IBM
IBM’s data quality solution comes as an on-premise or Cloud-based tool. It provides you with a wider and more comprehensive approach to data cleansing and data management.
It enables you to investigate, clean, and manage your data, assisting in maintaining consistent views of important entities, such as clients, suppliers, locations, and products. This solution enables you to produce high-quality data for your data warehouse, Business Intelligence, Big Data, application migration, and master data management initiatives.
10. SAS Data Management
Source: SAS
SAS Data Management comes with the industry’s leading integration technology for managing data integration and cleansing. It offers a role-based graphical environment. It features robust ETL and ELT tools, migration and synchronization capabilities, a metadata bridge for managing Big Data, and sophisticated tools for data governance and metadata management.
And there are more benefits from using this data integration tool, such as improved productivity and efficient work, easy collaboration with other teams, efficient metadata management, and more.
What Are Your Top Data Warehouse Tools?
A data warehouse is a repository of a large amount of historical data collected from a wide range of sources. It’s stored under a unified schema and usually resides at a single site.
A well-designed data warehouse is one of the most important factors in reporting and data analysis; it’s considered the core component of Business Intelligence. There are various tools involved in data warehousing, such as data modeling, data monitoring, data integration, ETL, and data quality tools. This article discussed the most popular data warehouse tools that you can use in data warehousing projects under different categories.
