Data Warehousing and Big Data Analytics may have seemed like a novel idea in the past, but today most critical tools needed to cater to various services are required by businesses worldwide. Data Warehouse Tools are essential for managing today’s Data Analytics process in firms of all sizes. These tools are compatible with various technologies such as Artificial intelligence and Machine Learning to improve performance.
This article lists the robust and popular Data Warehouse Tools used in the market. You will also gain a holistic view of Data Warehousing Tools and understand the need for these tools in Data Warehousing.
What are Data Warehouse Tools?
A Data Warehouse is a database designed to store large volumes of heterogeneous data. The data in a Data Warehouse is contributed by 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 to be analyzed easily.
Data Warehousing revolves 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 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. The best 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.
Why do we use Data Warehouse Tools?
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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 extract information from various sources and transform it before loading it into different data warehouses.
- Using data warehouse reporting tools, you can establish relationships within your data to do data modeling.
- Data warehouse automation tools enable querying your data to draw insights, analyze, and visualize it.
Read: Data Warehouse Best Practices: 6 Factors to Consider in 2024
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 14 Data Warehousing Tools that organizations can use to streamline their Data Warehousing workflows:
1) 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, and Databricks, 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.
Check Hevo’s in-depth documentation to learn more.
Get Started with Hevo for Free
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.
Hevo was the most mature Extract and Load solution available, along with Fivetran and Stitch but it had better customer service and attractive pricing. Switching to a Modern Data Stack with Hevo as our go-to pipeline solution has allowed us to boost team collaboration and improve data reliability, and with that, the trust of our stakeholders on the data we serve.
– Juan Ramos, Analytics Engineer, Ebury
Check out how Hevo empowered Ebury to build reliable data products here.
More reasons to Love Hevo
- Secure: Hevo has a fault-tolerant architecture that ensures the data is handled securely and consistently with zero data loss.
- Schema Management: Hevo removes the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional customer support through chat, email, and support calls.
- Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular time.
Sign up here for a 14-Day Free Trial!
2) Amazon Web Services Data Warehouse Tools
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AWS (Amazon Web Services) is one of the prominent leaders of Data Warehousing solutions. Throughout the years, AWS has introduced many services, making it a cost-effective, highly scalable platform. Let’s explore some of the popularly used AWS Data Warehouse Tools used:
- AWS Redshift: Amazon Redshift is a suitable fit for businesses that want high-level sophisticated capabilities, have the cash for a high-end tool and have an in-house team capable of managing AWS’s extensive menu of services. AWS Redshift provides SQL-querying of exabytes of structured, semi-structured, and unstructured data across the Data Warehouse, operational data stores, and a data lake. It also offers the option to aggregate data further using Big Data Analytics and Machine Learning techniques.
- AWS S3: Amazon Simple Storage Service (Amazon S3) is an object storage service that allows you to store and retrieve unlimited amounts of data from anywhere. It’s a low-cost storage solution with industry-leading scalability, performance, and security.
- Amazon RDS: Amazon Relational Database Service (Amazon RDS) is an AWS Cloud data storage service that allows you to run and scale a relational database. Its resizable and cost-effective technology allows us to create an industry-standard relational database and manage all database management activities.
3) Google Data Warehouse Tools
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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 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.
4) Microsoft Azure Data Warehouse Tools
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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.
5) Oracle Autonomous Data Warehouse
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Oracle Autonomous Data Warehouse is a Cloud-based Data Warehouse service that takes care of all the complexities of Data Warehouse development, data protection, and data-driven application development. The setting, safeguarding, regulating, scaling, and backing up of data within the Data Warehouse are all automated using this technology. A lot of self-service tools are put in to help Analysts, Data Scientists, and Developers be more productive. This new Cloud computing solution is easy to use, quick to respond to, and scalable. With this technology, keeping data protected from outsiders and insiders is simple.
Oracle Autonomous Data Warehouse Features
- Oracle Autonomous Database is core to the data lakehouse, serving as an analytical engine and data repository optimized for performance.
- The autonomous capabilities of provisioning, configuring, securing, tuning and scaling automate complex manual tasks prone to human error.
- The Oracle Autonomous Data Warehouse self-monitors system performance, auto-adjusting as workloads, queries and users change over time. This autonomous optimization ensures consistently high performance despite variations.
6) Snowflake
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Snowflake is a Cloud-based Data Warehouse Tool that offers a framework that is quicker, easier to use, and more adaptable than traditional Data Warehouses. Snowflake has a comprehensive SaaS (Software as a Service) architecture since it runs entirely in the Cloud. It makes data processing easier by allowing users to work with a single language, SQL, to do tasks such as data blending, analysis, and transformations on a variety of data types.
Snowflake’s multi-tenant design allows for real-time data exchange throughout your company. There is no need to relocate data. To ensure less administration and lower costs, Snowflake features auto-scaling (where you can automatically start/stop clusters) and auto-suspend (where you can stop the virtual warehouse after clusters have been inactive for a set duration).
Snowflake Features
- True separation of storage and compute: Snowflake uses innovative architecture that allows independent scaling of storage and compute resources. This provides flexibility and cost savings.
- Permanent data warehouse: Snowflake offers a data warehouse that is perpetually live and accessible without needing to rebuild or restructure it. This saves significant time and effort.
- Secure data sharing: Snowflake has extensive security and governance capabilities for easy, governed data sharing across multiple parties without copying data.
- Near-zero maintenance operations: Snowflake is fully managed, requiring near-zero maintenance, tuning, optimization, ensuring resources focus on extracting value rather than admin tasks.
7) IBM Data Warehouse Tools
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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 quickly. 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.
8) Teradata Vantage
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Teradata Vantage is a Cloud Analytics platform that combines Analytics, Data Lakes, Data Warehouses, and new data sources, among other platforms. It provides an all-in-one solution for enterprises of all sizes, as well as comprehensive Analytics. When dealing with massive amounts of data, it provides linear scalability by adding nodes to improve the system’s performance.
SQL is supported by Teradata Vantage for interacting with data stored in tables. It can distribute data to discs without the need for manual intervention. It is based on MPP (Massively Parallel Processing Architecture), which breaks a large job into smaller ones and processes them all at the same time.
Teradata Vantage Features
- Leverages powerful parallel processing for super-fast query speeds and performance at scale.
- Specifically engineered to provide best-fit analytics perfectly matched to wide-ranging business needs.
- Scalable cloud infrastructure seamlessly expands and optimizes to meet analytical requirements.
- Implements defense-in-depth strategies and advanced security measures.
- Natively integrates with commercial, open-source tools and languages for diverse analytics.
- SQL-based architecture provides simplicity and ease of use for accessing full capabilities.
9) SAS Cloud
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SAS (Statistical Analysis Software) is a Data Warehousing solution that enables users to retrieve data from a variety of sources. SAS simplifies the process of analyzing large amounts of data. It also offers data that can be shared between enterprises and managed using numerous information tools and reports.
SAS has a built-in Quality Knowledge Base (QKB) for storing and processing data. SAS activities are administered from a central location, so users can use the tool from anywhere as long as they have an internet connection.
SAS Cloud Features
- Unified Environment: SAS Cloud combines data management, analytics and AI workloads in a single, integrated cloud environment. This eliminates silos and connectivity challenges.
- Automation: SAS Cloud has in-built automation capabilities ranging from data management and data quality to model deployment. This reduces manual tasks and allows focus on value-add.
- Agility and Scalability: The cloud-native architecture allows dynamic scaling to handle unpredictable business volumes and variations. This provides agility.
- Low-code Environment: The drag-and-drop workflow interfaces and automation reduce need for coding expertise and skills. This makes analytics more accessible.
10) SAP Data Warehouse Cloud
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SAP Data Warehouse Cloud is an integrated Data Management platform that maps all of an organization’s business operations. It’s a high-end application package for open client/server platforms. It’s one of the greatest Data Warehouse Tools in the market. It has established new benchmarks for giving the best commercial Data Management and Warehousing solutions.
SAP Data Warehouse offers business solutions that are both very adaptable and transparent. It is designed in a modular format for ease of setup and efficient use of space. You can build a database system that incorporates both Analytics and Transactions. These next-generation databases are portable and can be used on any device.
SAP Data Warehouse Cloud Features
- Unified semantic data model: It provides a unified semantic layer and data modeling capabilities that span multiple data sources. This enables easy consolidation of data from diverse sources.
- Smart data integration: It has pre-built connectivity and data integration capabilities that use machine learning to simplify and automate complex data integration tasks. This includes smart data mapping, smart data quality checks etc.
- Hybrid and multi-cloud support: It can run across public clouds like AWS, Azure, GCP and private clouds to support hybrid and multi-cloud needs.
11) PostgreSQL Data Warehouse Tool
PostgreSQL is an open-source and cloud-based database management system. It is an enhanced version of SQL and facilitates various functions of SQL like foreign keys and subqueries and other user-defined functions. It supports SQL and JSON querying. It can handle large volumes of data. With its authentication features it is quite a secure data warehouse tool. Further, PostgreSQL is a simple and powerful data warehouse solution because of its faster data reading and writing speed.
PostgreSQL Features
- Scalability: Designed for handling large data volumes, PostgreSQL allows scaling up and out to accommodate increasing workloads via partitioning, replication, and horizontal scaling.
- Robust Security: PostgreSQL offers various security mechanisms like authentication, access controls, encryption that enable secure data warehousing.
- Flexibility & Power: It provides a highly flexible and powerful solution for analytics and data warehousing needs with rich features as well as ease of use and management.
12) Micro Focus Vertica
It is an SQL data warehouse that uses MPP to speed up querying. It is quite simple to use and is highly scalable. Micro Focus Vertica is a column-based relational database that groups data together before storing it on a disc by column. It enables predictive maintenance and network optimization for business enterprises. Vertica has built-in analytics capabilities, including machine learning and time series. It enables standard programming interfaces like OLE DB. To optimize storage, it deploys compression.
Micro Focus Vertica Features
- Optimized for Analytics: Vertica leverages columnar storage and Massively Parallel Processing (MPP) architecture to provide fast query performance vital for data warehousing workloads.
- Flexible Scalability: Its shared-nothing architecture allows linear scaling of storage and processing power to handle increasing data volumes and user concurrency.
- Efficient and Secure: It offers built-in analytics capabilities, standard interfaces, and compression to accelerate secure analysis while optimizing storage.
13) DynamoDB
Amazon DynamoDB is a NoSQL data warehouse service that supports key-value and document data structures. It has an identical data model and encompasses a completely different underlying implementation. DynamoDB has a partition key value that can be used as input for an enclosed hash function. Its output decides the partition in which the item will be kept. All items that have identical partition key values are stored together.
You can get progressive scalability while using DynamoDB. Used for OLTP use cases, it enables high-speed data access when there is a need for operations on many records simultaneously. It allows automatic scaling per your application load, pay-per-what-you-use rating, and no servers to manage. Thus, DynamoDB can be used as Serverless applications.
14) Cloudera
Cloudera is a Data Warehousing Platform that offers multi-functional analytics. A cloud-based platform, it removes data silos and makes drawing data insights faster. Whether your data is on private, public, or hybrid clouds, it helps you secure and manage all your data. It is cost-efficient as it handles data from the edge. You can infer business insights through artificial intelligence and machine learning using its augmented tools. This includes Data Visualization, Hue, and Workload XM, which have made querying and visualization easier.
Critical Factors to Consider while Selecting the Right Data Warehouse Tool
Choosing a Data Warehouse Tool that meets all of your company’s demands necessitates careful consideration. When selecting a Data Warehousing Tool, keep the following 4 aspects in mind:
1) Cloud vs On-Premise
The first consideration when selecting a Data Warehouse Tool is whether to employ Cloud or On-Premise Data Warehouse Solution. A Cloud Data Warehousing solution is the best option if you want a low-cost effective solution with no extra servers, hardware, or maintenance fees.
On the other hand, if data security is a top issue for your company, On-Premise Data Warehouse design may be the best option since it allows you complete control over data security and access. However, this solution isn’t cost-effective and requires high maintenance.
2) Performance and Scalability
Data Warehouse Tools provide varying levels of performance. You should use a solution that guarantees that your data is cleaned, de-duplicated, transformed, and loaded appropriately to keep your Data Warehouse performing at its best. Moreover, you should select the tool which scales with your business needs.
Some Data Warehouse Tools and Storage are horizontally scalable, which means they provide optimal performance even as your Data Warehouse grows in size. Furthermore, if properly tuned, such Data Warehouse Tools can be cost-effective.
3) Integrations
Integration of many data sources, such as Cloud sources, streaming applications, and databases, is common in business development, resulting in huge amounts of heterogeneous data. In this case, choosing a Data Warehouse Tool that can combine data from many applications and information systems is critical.
4) Use Case
It doesn’t matter how powerful a Data Warehouse tool is if it isn’t tailored to your company’s needs. Some tools excel at handling large datasets, while others excel at handling smaller ones. Consider the kind of data you’ll be working with the most while assessing your options. If your data is currently kept in a number of systems or formats, find a solution that can handle the increasing complexity.
5) Cost
You need to keep in mind several things when it comes to cost when choosing a solution for yourself from different vendors of data warehouse tools. You need to remind yourself of your requirements, such as what you want from the tool, the volume of your data, and its maintenance requirements. There are many open-source tools for BI and reporting that you can use but they require skilled developers for maintenance and coding. When it comes to storage, cloud-based tools are best as they provide scalable storage and charge based on the amount of data you are storing.
6) Automation
Automation is essential in today’s fast-paced world. Many data warehouse tools save you time and money while reducing security risks. Traditional data warehousing tools offer automation at every step with workflow automation and data model design patterns. It reduces the cumbersome process of SQL querying by ensuring that the design, data mapping, and ETL code are generated automatically.
Conclusion
In this article, you gained a basic understanding of Data Warehouse and working with Data Warehouse Tools. You 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 Data Warehousing Tool. Furthermore, this article provided a comprehensive overview of 10+ popular Data Warehouse Tools used in the industry.
Extracting complex data from a diverse set of data sources like Databases, CRMs, Project management Tools, Streaming Services, Marketing Platforms can be quite challenging. This is where a simpler alternative like Hevo can save your day!
Hevo Data is a No-Code Data Pipeline that offers a faster way to move data from 150+ sources, into your Data Warehouse such as Amazon Redshift, Google BigQuery, Snowflake to be visualized in a BI tool. Hevo is fully automated and hence does not require you to code.
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Shubnoor is a Data Analyst with extensive expertise in market research, and crafting marketing strategies for data industry. At Hevo, she specialized in developing connector integrations and product requirement documentation for multiple SaaS sources.