Data Warehouse: 7 DW Tools That Are Trending for 2022

min

Created in:

Jan 13, 2022

Updated:

9/27/2024

Tools

Data warehouse

We take a look at the pros and cons of these data warehouse (DW) tools that are trending for 2022:

Azure Synapse Analytics
Amazon Redshift
Oracle ADW
Google Big Query
Snowflake
PostgreSQL
Azure Databricks

Experience a digital transformation: implement a DW tool to work well with your data, and your company's future will be much more prosperous and automated.

Learn now about data warehouse tools so you can quickly have your data available when and where you need it in 2022.

Happy reading! 😉

What is a data warehouse?

Also known as the home of data, a data warehouse is the database that stores and integrates structured data in one place. Widely used in business intelligence (BI) processes, DW is optimized and designed for analytical queries.

Because data from disparate sources doesn't integrate naturally, the data warehouse offers this major advantage by consolidating that information from disparate sources into one centralized location, making the data clearer to read.

Why implement a data warehouse?

In a world where customer data is crucial for medium to large businesses, a data warehouse becomes vital. In addition to storing information from different sources in the same place and assisting in decision-making, a DW also has the functions of:

generate strategic and operational insights;
ensure an analytical basis for decision-making;
evaluate and measure the impact of marketing campaigns;
analyze the performance of employees;
Monitor market trends.

DW tools are used to run a process called ETL:

E = extract the relevant data directly from the source;
T = transform the data and ensure compatibility with DW;
L = load the data into the data warehouse to be analyzed.

This process can be done in a more modern way, say, by reversing the last two steps of it. In this way, it is called ELT.

In one way or another, there are DW tools that are better at some steps of the process than others. And there are even options with broader functionalities.

We take a look at the pros and cons of these data warehouse (DW) tools that are trending for 2022:

Azure Synapse Analytics
Amazon Redshift
Oracle ADW
Google Big Query
Snowflake
PostgreSQL
Azure Databricks

Get to know each of them now so you can quickly have your data available when and where you need it in 2022. Let's get to the list!

Data Warehouse Tools: Trends for 2022

We have listed the DW tools that can help in the decision-making of managers of companies of all sizes around the world. Check out the descriptions, pros, and cons.

1- Azure Synapse Analytics

Azure Synapse Analytics offers unlimited analytics services, bringing together data integration, data warehousing, and big data analytics into a single software. The idea is to give professionals the freedom to query data the way they want, with serverless or dedicated options, at scale.

Combining and modeling data is made easier with the database templates available in the program. And there are tutorials and training modules for data engineers. The limitless scale is a big draw for those extracting data-driven insights within DWs.

Pros

It integrates seamlessly with other Azure data services, enabling unified data analytics.
Microsoft Azure is built around various use cases, so it's able to come up with ready-made solutions for anything.

Cons

Many SQL syntax features are not available, and there are no deduplication features in storage. High coding and knowledge for traditional features of other DW systems and no conversion tools for code.

2- Oracle ADW

Oracle Autonomous Data Warehouse is a cloud-based DW service that prioritizes eliminating the complexities of operating a data warehouse and other data-driven services. DW provisioning, configuring, securing, tuning, scaling, and backing up are automated.

It has comprehensive protection and offers a complete solution of self-service tools and advanced analytics, using a converged database with built-in support for multi-model data and diverse workloads.

Pros

The autonomous nature of the Oracle database helps reduce ongoing maintenance expenses.
Database performance is remarkable for analytics workloads after installing Oracle ADW.

Cons

Integration with SQL server databases requires a certain amount of effort to refactor.
The high cost and support offered are not the best attractions of the service.

3- Amazon Redshift

One of the top-rated DW tools by online users. Just as the name suggests, Redshift is part of the web services offered by Amazon's cloud platform. Operates as a petabyte-scale data warehouse completely managed in the cloud.

The service allows analysts to run queries in a matter of seconds, because the tool continues to update the data pool, precisely so that connections can be reused when replicating information from failed drives and replacing it when necessary.

Pros

Automates administrative tasks such as managing, monitoring, and scaling the data warehouse.
Allows you to run queries on unstructured data, which saves you a lot of time

Cons

It doesn't offer a multi-cloud solution, it's only available on Amazon Web Services (AWS).It is known to have issues with efficient storage handling.

4- Google Big Query

Serverless, cloud-based data storage tool offered by Google. It is capable of storing large amounts of data and uses SQL in its queries.

Big Query is efficient in generating insights through the information collected.

Big Query's interactive indexing system allows for very fast and complete queries. The service is interesting for companies that use their own data and that deal with various types of data management between their teams.

Pros

The data can be analyzed in real-time for up-to-date information.
It allows you to analyze petabytes of data at an efficient speed, in addition to having a great cost-benefit ratio.

Cons

Using Big Query can be complex for those just starting out, especially because of the user experience that is compromised by the tool's interface. Operating a Big Query API requires programming knowledge, which can make it difficult for professionals outside of these areas to handle.

5- Snowflake

Snowflake is a cloud-based platform that provides data warehouse services for both structured and semi-structured data. The architecture of this tool allows storage and compute to be scaled separately.

This provides data scientists, analytics, and business intelligence professionals with access to more than 375 query-ready data sets.

Snowflake is used especially by those looking for a scalable, efficient, and easy-to-use tool.

Pros

Its cloud has an elastic nature, which means that a large amount of data can be stored and multiple queries can be executed simultaneously.
The unique feature is that combined structured and semi-structured data can be loaded into the cloud database without turning into a fixed category.

Cons

Snowflake has a high price compared to other tools, making it not a very affordable option.
The interface is not very intuitive and has navigation errors.

6- PostgreSQL

It is quite a popular open-source tool that stores, integrates, and analyzes data using built-in features and analysis instruments. Procedures and functions can be created in various programming languages, such as PL, pgSQL, Python, etc.

It serves as a low-cost, straightforward, and efficient data storage solution. Its installation is easy and the use of this manager is practical, being able to increase the number of features through the extensions available for the service.

Pros

You can combine PostgreSQL with external tools and applications for data mining and reporting.
There is a huge amount and variety of third-party extensions for PostgreSQL, both free and paid.

Cons

PostgreSQL does not provide data compression capabilities, which makes it difficult to study and perform.
It has considerably constant instabilities, especially in queries that end up being slow.

7- Azure Databricks

Azure Databricks is Microsoft's optimized data analytics platform, integrated with the company's cloud services. Although it is not necessarily a data warehouse, the tool combines the functions of DW and data lake for enterprises.

Databricks offers three data-driven application development environments: Databricks SQL, Databricks Data Science Engineering, and Databricks Machine Learning.

Pros

Excellent development environment and user-friendly interface that is easy to handle.
Improved performance by consolidating small files into delta tables.