In the age of big data, where humans generate 2.5 quintillion bytes of data every single day, organizations like yours have the potential to harness more powerful analytics than ever before. But gathering, organizing, and sorting data still proves a challenge. Put simply, there's too much information and not enough context. The most popular commercial data warehouse solutions like Amazon Redshift say they deliver structured, usable data for businesses. But is this true?
AWS Redshift claims it's the fastest cloud data warehouse on the planet, with "up to 3 times better price-performance than any other data warehouse." But these results depend on the size and scale of your business, and other solutions could prove far more effective. In this guide, we compare Amazon Redshift with other data warehouse platforms like Snowflake and Azure Synapse Analytics.
Table of Contents
- Amazon Redshift Data Warehouse Overview
- Benefits of AWS Redshift Data Warehouse
- Cons of Amazon Redshift Data Warehouse
- Comparing Amazon Redshift to Traditional Data Warehouses
- How to Choose the Right Data Warehouse
- Integrate Integrate.io With AWS Redshift For the Best Data Warehouse Solution
Amazon Redshift Data Warehouse Overview
Amazon Redshift functions differently from traditional data warehouses. Other platforms, like Snowflake, that store data in rows, can cause multiple queries when arranging and sorting data. Redshift is a column-oriented database management system, which means it stores data in columns, making it quicker to analyze data. (Amazon Redshift says it's 2 times faster than competing products like Snowflake.)
AWS Redshift lets you store data in a data lake before the data goes to the data warehouse. (A data lake contains raw, unsorted data; a data warehouse contains structured data.) One Amazon Redshift user says the ability to integrate data with a data lake has allowed them to "integrate new data sources within hours instead of days or weeks." Another user, posting on the software review website G2.com, says the platform is "easy to learn" and "simple to use," and processes data "fast." (AWS Redshift currently has an average user score of 4.3/5 on G2.)
For more information on Integrate.io's native Redshift connector, visit our Integration page.
Recommended Reading: Integrate.io's Comprehensive Guide to Amazon Redshift
Benefits of AWS Redshift Data Warehouse
As mentioned above, AWS Redshift offers users fast speeds. But it also scores points among users for value for money, providing organizations with a fast and powerful data warehouse at a comparatively modest cost. Prices start from $0.25 per hour, cheaper than Snowflake's $2.01 per hour.
There are other benefits as well. You can customize AWS Redshift with additional nodes to increase power for large data sets, letting you analyze data without reducing query response times. Amazon Redshift also comes with security protocols to protect sensitive data, such as the following:
- SSL encryption for data in transit.
- Encryption for client-side and server-side data.
- Column-level access control.
- Access management.
- Sign-in credentials.
Cons of AWS Redshift Data Warehouse
Conversely, AWS Redshift might be too easy to scale, with the ability to improve disk space and power via a few modifications to the AWS Console. At least one Redshift user has written about this online. We think:
- While quick scalability might prove invaluable for users with particularly large volumes of data, most companies won't benefit from this at all.
- If Amazon Redshift is too robust, it might cost you more money in the long-term.
- Redshift does let you scale back processing speeds and power, but you could then experience a spike in volume that the system won't be able to handle.
Comparing Amazon Redshift to Traditional Data Warehouses
Traditional data warehousing techniques are designed to support programmed functionalities such as:
- Roll-up: Data is generalized by summarizing it
- Pivot: Cross tabulation (rotation) is performed
- Slice and Dice: Performing projection operations on the dimensions
- Drill-down: Revealing more details
- Selection: Information is available by value and range
- Sorting: Data is sorted by ordinal value
The core benefits of data warehousing are as follows:
- A collection of information for competitive and comparative analysis.
- High-quality level of information enhancing completeness.
- Disaster recovery plans with any other data backup source.
Amazing Redshift uses columnar storage technology in order to improve I/O efficiency and parallelized queries across multiple nodes and provide fast query performance. The service also offers custom ODBC and JDBC drivers which a developer can easily download it from the Connect Client tab of Console. It allows you to access the wide range of familiar SQL clients.
As impressive as Amazon Redshift is, it's just one data warehouse solution on the market, competing with the following products (among others):
- Azure Synapse Analytics (formerly known as Microsoft Azure SQL Data Warehouse)
- SAP Business Warehouse
- Google BigQuery
Both individual users and expert analysts have compared the above data warehousing solutions on features, capabilities, pricing, and other factors. User reviews on G2 reveal the following about AWS Redshift alternatives:
- Microsoft Azure Azure Synapse Analytics: Azure is not as cost-effective as Redshift but provides better support. (Average user score: 4.4/5.)
- Snowflake: More flexible and usable than Redshift. (4.6/5.)
- SAP Business Warehouse: More expensive than Redshift. (3.7/5.)
- Oracle Autonomous Data Warehouse: More expensive than Redshift. (4.5/5.)
- Google BigQuery: More intuitive and easier to administer than Redshift. (4.4/5.)
Recommended Reading: AWS Redshift vs. Google BigQuery
Reviews and use-cases can be subjective, however, and you shouldn't rely on them when choosing a data warehouse. For example, G2 reviewers say Azure Synapse Analytics is more expensive than Redshift. But a recent review from business analytics consultancy Think Big Analytics had high acclaim for Azure, praising its security features, easy-to-use API, powerful data insights, and unified experience:
"Azure Synapse Analytics Data Warehouse software enables users to query their data on their terms and a limitless scale."
How to Choose the Right Data Warehouse
As is the case when choosing any commercial application, you must start with a clear understanding of your business requirements. Then ask yourself the following questions:
- Do you expect to scale up data processing and data storage?
- Do you experience spikes in data volume and require a customized solution?
- What's your budget?
- Do you need accessible support from your solution provider?
- Do you need an intuitive and user-friendly solution?
Develop a framework for data processing requirements, and you'll find a data warehouse solution with cloud services that provide the right amount of power, functionality, and high performance for data analytics.
Integrate Integrate.io With AWS Redshift For the Best Data Warehouse Solution
There's one last thing to consider when deciding whether Amazon Redshift is the best data warehouse for you: How do you move all your data into Redshift in the first place? Integrate.io's Redshift data integration tool lets you transport data into Redshift via the Extract, Transform, and Load (ETL) process. It works like this:
- Extract data from various sources (SaaS systems, legacy systems, apps, etc.).
- Transform data into a readable, usable format.
- Load data into AWS Redshift.
All of this eliminates slow database queries on Redshift and executes better data analytics. Once you have extracted, transformed, and loaded data into AWS Redshift, you can connect Redshift to business intelligence tools such as Looker and Tableau for unparalleled data algorithms and real-time insights, helping you make smarter decisions.
Learn more about Integrate.io's Redshift integration here.
Want to learn more about how Integrate.io is the best ETL data pipeline for Redshift? Contact us to schedule a demo and 14-day risk-free pilot and experience the platform for yourself.