• Navid Nassiri

Is it time to upgrade your legacy data storage solution?


As a data-driven business, you collect and store data from many different sources. But for the data to be useful, you cannot analyze it in silos. Instead, you need to unify those data streams into one place, so it becomes foundational data within a data lake or warehouse. And you’ll need to use ETL tools that scale with the data as it grows.


Whether opting for a “do-it-yourself” approach by building custom software, or taking advantage of turnkey data automation solutions, your business and tech teams still need a data warehouse (usually cloud-based) to house their data.


In this post, we take a look at three market-leading solutions: Google’s BigQuery, Snowflake, and Amazon Redshift.


Best data warehouses for ETL (and ELT)


Before diving into tool, you need to decide on your processing strategy, i.e. ETL vs ELT. The main difference between the two is which part whether the data is transformed before or after loading into your data warehouse. Then there are few technical aspects to consider, for example, OLAP (Online Analytical Processing) warehouses only accept SQL-based data, while some apps store data in rows rather than columns, and so on.


READ MORE: What is the difference between ETL and ELT?


So let’s take a look at the top data storage solutions available.


Snowflake


One of the newest data warehouse solutions on the market, Snowflake officially launched in 2014 with an IPO in 2020. Snowflake, like the other tools here, is cloud-based and can scale your data instantly with various packages and pricing to suit your business needs. You can use Snowflake to store and access unstructured, structured, and semi-structured data in one place as well as acquire third-party datasets without the need to move or copy data.


Businesses can also use Snowflake to apply dedicated compute clusters to any workload in almost unlimited quantities, and when the workload is done, it automatically dials down so you don’t pay for overprovisioning.


BigQuery


Launched back in 2011, Google’s BigQuery is one of the more established data warehouses on the market today. It’s an analytical warehouse that can hold an unlimited amount of data all in the cloud. One of the core strengths its customers enjoy is its speed; it can query terabyte-scale datasets in seconds using SQL. Its pricing is also based on how much data you query and when you query it.


The most important consideration you need to make with BigQuery is whether your data needs to change over time. While BigQuery can consolidate and query large amounts of data at lightning speed, it’s not designed to automatically handle changing data, so you’ll need some manual intervention if you have data that is prone to constant change. Also, if you want to make sure you’re getting the most use out of your Total Cost of Ownership, you’ll need developer expertise to manage it, whether in-house or as part of your automated ETL solution provider.


Redshift


The final tool in our round-up is Amazon Redshift. Launched in 2013, it’s hosted using the Amazon Web Services (AWS) cloud ecosystem - which means it has easy integration with any AWS products your teams might already be using. Unlike other data warehouse solutions, Redshift is a Relational Database Management System (RDBMS) built on PostgreSQL and designed specifically for OLAP.


One of the biggest differences between Redshift and the other tools is its pricing. Unlike BigQuery (for which you pay by the query), with Redshift, you pay by the instance. This means you’ll more likely need a dedicated administrator to handle large volumes of instances, which can be complex without the right domain expertise in place.


How Switchboard can help


While data warehouses help you store your data, you still need an ETL tool to get your data to the warehouse and process it. Switchboard helps enterprise tech and business teams aggregate their disparate data at scale in a no-code environment - in their data warehouse of choice - without the need for in-house engineering.


This means business teams can get straight to the data they need, in real time, for more accurate insights. They also benefit from automatic monitoring to detect bad APIs and backfilling to re-pull any missing data.


Is Power BI a data warehouse?


No, Power BI isn’t a storage solution, but rather a visualization tool that sits on top of your data ops stack, providing dashboards to help you see your data clearly. Other visualization tools include Lookr and Tableau. Switchboard integrates easily with these tools so you can get the best view of your data when you need it.


Cloud-based data warehouses like Snowflake, BigQuery, and Redshift are great options for storing and analyzing your data. However, each one needs custom development support to help your teams continually manage the data over time.


To learn more about how to build an ETL pipeline using your chosen data warehouse, take a look at our ultimate guide, or contact our team to learn more.

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