We now generate more data in an hour than we did in an entire year just two decades ago. The same old tools simply don't cut it anymore. That's why Amazon came out recently with new tools like AQUA for Amazon Redshift, AWS Glue Elastic Views, and Amazon QuickSight Q. The primary aim is to make it much easier to move and combine data from disparate sources (and derive more value through machine learning).
These new AWS tools promise to help customers get more value from their data at any scale, quickly and easily. However, if you're still not sold on Amazon Redshift, there are plenty of alternatives to choose from.
But identifying the right tool for your next data project demands some effort. The good news is that we have compared some of the key differences, including price, ease of use, and features. Here are the top 5 alternative tools you should consider before making the switch from Redshift:
1. Panoply
Panoply is an all-in-one data warehouse and code-free ETL tool that's easy to set up, manage, and maintain.
This means that you can easily sync, store, and access all your data in a centralized platform. As it comes with several pre-built connectors, you can make your data accessible to a host of analytics tools or query data directly within Panoply.
One factor that sets Panoply apart from its competitors is its best-in-class support. While it's simple enough for even less technical users to set up, you have the confidence of knowing there's someone on the other end of the line if you run into trouble.
Panoply pricing: see all pricing options; a free trial is available.
2. BigQuery
BigQuery is a highly versatile tool to ingest and process queries at scale. If you're on Google Cloud, it doesn't require much effort to integrate it with other Google products.
You can run queries or analyze terabytes of data in seconds. BigQuery lets you leverage the power of Google's infrastructure to load data. You can also use Google Cloud Storage to bulk load your data, or you can stream it in bursts of up to a thousand rows per second.
It's supported by the BigQuery REST API that comes with client libraries like Java, PHP, and Python. While BigQuery is the most proven tool on this list, it's not the easiest to use. If your team lacks an experienced data engineer, you're going to have problems as the learning curve is significant.
BigQuery pricing: query based on the amount of data processed at $5 per TB (and includes one free TB per month).
3. Snowflake
Like Redshift, Snowflake is a robust cloud-based data warehouse built to store data for effortless analysis. Developed for experienced data architects and data engineers, Snowflake leverages a SQL workbench and user permissions to allow multiple users to query and manage different types of data.
Snowflake also boasts robust data governance tools, security protocols, and the rapid allocation of resources. While the platform is powerful and efficient at managing different data types, it still proves to be a significant challenge for users who don't hail from a strong data background.
Snowflake also lacks data integrations, so your data teams will have to use an external ETL to push the data into the warehouse. Whenever you use third-party tools, you'll also have to consider the extra costs and overheads (such as setup and maintenance costs) that come with them.
Snowflake follows a consumption-based pricing model similar to that of Redshift. This is great for experienced users who can make an educated guess about this data consumption. Others may have to deal with an unpleasant surprise at the end of the billing cycle.
Snowflake pricing: based on a per-second data consumption model (with an option of a 30-day free trial).
4. PostgreSQL
PostgreSQL may be open source, but it has been a dominant force for decades, maintaining its place as a top SQL server that boasts high performance and reliability. While it isn't as fast as MongoDB, this object-relational database system continues to rule this space (and even Redshift is based on Postgres!). It remains a popular choice among app developers and large corporations.
Like Snowflake, PostgreSQL requires an external ETL tool to get your data out and analyze it. The administration tools that come with Postgres are powerful, but you'll have to contend with a steep learning curve.
As a result, Postgres' popularity is driven by seasoned data engineers who love its flexible configuration options and more, but others may find themselves paralyzed by the complexity of its setup and maintenance protocols.
Postgres pricing: open-source and free (to those who are ready to manage everything themselves).
5. Azure SQL Data Warehouse
Azure SQL Data Warehouse is perfect for large businesses dealing with consumer goods, finance, utilities, and more. As one of the most used services on Microsoft Azure, it's a SQL server in the cloud but is fully managed and more intelligent.
Now absorbed into Azure Synapse Analytics, it's a powerful cloud-based analytics platform you can use to design the data structure immediately (without worrying about potential implementation challenges). Its provisioned resources also allow users to query data quickly and at scale.
If you're not familiar with the Azure environment, you'll have to invest some time in understanding it. As it's fully featured and well documented, there's enough support to get you over the learning curve.
Like Redshift and Snowflake, Azure Synapse also follows a consumption-based pricing model. So, it's best to have an experienced data engineer on-board to make "reasonably accurate guesstimates" before committing.
Azure pricing: follows an hourly data consumption model (and offers a 12-month free trial).
Comparing Data Warehouses
As data continues to drive businesses forward, storage platforms are only going to get better. As this space becomes more competitive, you'll have access to more innovative technologies cost-effectively. But for now, you'll have to choose the right storage for your next project based on your unique requirements, in-house expertise, and costs.
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