- Navid Nassiri
Top data warehouses of 2022
Data is the life-blood of any digital business, but to make use of it, you need to analyze it effectively. And to do this, you’ll need to use an ETL pipeline to unify data into a data warehouse or data lake, to create foundational data.
But regardless of whether you build your own in-house data automation software, or make use of an off-the-shelf solution, you’ll still need to choose a data warehouse to host your data.
There are a few considerations before you can decide on a data warehouse. For example, is it best to process your data using ETL or ELT? Is all of your data in SQL (since OLAP data warehouses only accept this format)? Is your data stored in rows or columns?
Now let’s take a look at the top data warehouse solutions our customers are using.
BigQuery was launched in 2011 and offers a practically unlimited amount of cloud storage combined with an inbuilt query engine. Query data can be accessed using SQL, but the warehouse’s hybrid nature means you can also query non-SQL data. BigQuery is popular among data scientists who manage huge datasets, as well as ML modeling, as it can run queries very quickly.
BigQuery functions as the core data warehouse, but to create an end-to-end data solution, you’ll need to connect additional services, such as Google Cloud Dataflow, Cloud BigTable, Cloud DataPrep, and Cloud SQL. Also, since BigQuery has a few different features to conventional platforms, data teams may find they have a steeper learning curve.
Snowflake is a relatively new data warehouse, having launched in 2014. It’s cloud-based, but isn’t tied to a single platform, so can run on different services, such as AWS, Google Cloud Platform, or Microsoft Azure. It’s a versatile tool, and can deal with structured, unstructured, and semi-structured data in one place.
Since Snowflake is cloud-based, it can quickly and easily scale storage to suit your data needs. It can compute independently of storage, meaning that if your number-crunching needs are out of proportion to your storage needs, you won’t be paying for unused capacity.
The first full release of Redshift was in 2013, as part of the larger AWS platform. This means you can easily integrate it with other AWS products you might be using. Redshift is an RDBMS (Relational Database Management System) which is built on PostgreSQL and uses OLAP specifically.
Instead of paying per query, as with other EDWs, payment is per instance, at an hourly rate. This can make Redshift cheaper than competitors, but managing these instances can become complex, so you may need to assign a dedicated member of staff with the necessary expertise to handle them.
Azure was released in 2010 and is a multi-faceted cloud-computing platform, offering specific technologies in addition to data storage, such as AI, ML, IoT, and blockchain. Integrations are naturally geared toward Microsoft tools. A major strength lies in Azure’s monitoring features, and the ability to easily configure for higher availability.
Some may find Azure complicated to use, since it requires a lot of configuration. There is a consensus that its lower-tier customer support could be better, so you may prefer to pay for the premium service.
Panoply uses the Amazon Redshift Data Service, Elasticsearch Database, Amazon S3 storage, and Spark Compute Architecture. Since it runs completely on AWS, this architecture enables you to add more nodes to your cluster for easy scaling.
Data results are materialized and saved to avoid recomputing transformations, so if data has changed, you’ll need to manually refresh the results to get updated information. Panoply is actually a data warehouse and an ETL platform combined, so if you already have an ETL solution, it may not be a suitable choice.
How Switchboard can help
Whichever data warehouse you choose, you still need a solid ETL tool to collect, process, and situate the foundational data in storage. Switchboard provides a data unification platform that helps enterprises aggregate disparate datasets at scale, without the need for coding or in-house engineering. And it can use the data warehouse of your choice.
Your business team will be able to access the desired data in real time, and gain more accurate and timely insights. Switchboard also provides automatic monitoring to detect issues such as faulty APIs, and offers backfilling to re-pull any incomplete data.
To learn more about how to build an ETL pipeline using your chosen data warehouse, check out our ultimate guide, or contact our team to ask any questions.