Understanding and Evaluating Cloud-Based ETL Tools
With things like business intelligence (BI), data-driven strategies, and comprehensive analytics becoming increasingly integral parts of today’s long-term business strategies, it’s no surprise that ETL platforms hold a more prominent role than ever.
So what is ETL, what are your ETL options, and how do you find the best choice for your business? Let’s break it down.
ETL and Your Business
Do You Need an ETL Tool?
Extract/Transform/Load (ETL) platforms have long been a staple tool for many businesses working with big data. More recently, however, they’ve also begun to take center stage with small-to-medium sized businesses as these companies try to wrangle their data sources and make the most out of the information at hand.
So how does it work, and how do you know if you need ETL for your business?
As the name implies, ETL is a three-step process by which users turn disparate data streams into clean, organized data sets. Here’s how it works: users extract data from source systems, enforce data quality and consistency standards, conform the data so that separate sources can be used together, and deliver the data in a clean, consistent format so that it can be used to make decisions and improve strategies.
Here’s what happens during each stage:
Extract: Data is extracted from a business’ important data sources, including their CRM, social medial, legacy systems, etc. At this stage, you not only determine your sources, but also things like the refresh rate (velocity) of each source, and priorities (extract order) between sources – all of which heavily impact time-to-insights.
Transform: The extracted data is then delivered to an interim staging area, where it is converted into usable formats by cleansing, qualifying and combining data. For example, dates are consolidated into specified time buckets, transactions are modeled into events, location data is translated to coordinates, etc.
Load: The transformed data is uploaded to a new home, or destination, where it can be mined for BI and used to improve operations.
In the big picture, this process saves significant time on data extraction and preparation - time that could be better spent on conducting analytics and gaining actionable insight. This process also performs a number of important functions that can help you better organize and understand your data, including:
Parsing/Cleansing – Data generated by applications is created in various formats like JSON, XML or CSV. During the parsing stage, data is mapped into a table format with headers, columns and rows, and the specified fields are extracted. That way, you can merge it and understand it more comprehensively overall.
Data Enrichment – In order to prepare data for analytics, certain enrichment steps are usually required, including: filling in missing data, fixing duplicate data, geo modifications, matching between sources and more.
Setting Velocity – Velocity refers to the frequency of data loading, whether new data should be inserted, or if existing data needs to be updated.
Data Validation - There are cases where data is empty, corrupted, missing crucial elements, too thin or too bloated. ETL finds these occurrences and determines whether to stop the entire process, skip it or set it aside for inspection while alerting the relevant administrators.
If you would benefit from these functions - or if your business is dealing with things like inconsistent data, hand coding, compliance issues, or data-related SaaS problems - then ETL tools might be a good choice for your business.
Choosing the Right ETL Tool
Now that you understand what ETL can do for your business, it’s time to go over how to find the right ETL tool for you. Here are some key features and considerations to keep in mind:
Consider Your Destination
ETL tools don’t come with a destination or data warehouse solution (DWH) built-in. That means you’re either going to have to use an existing database - if you have one available - or you’re going to have to set up a new DWH to house your ETL data. There are lots of considerations to keep in mind here.
Most importantly, you have to:
Determine your schema design - aka how your warehouse is going to be organized and used.
Choose between cloud vs on-premise warehouse tools - learn about what to consider when selecting a data warehouse.
Decide if you want to manage your warehouse on your own or use a data warehousing service.
Determine what database size is right for you.
Figure out how much you need to scale.
Overall, make sure you have your destination set up and ready to go before you begin with ETL.
Think About Internal Bandwidth
Using a tool that requires constant coding and engineering resources can be a big long-term problem. That’s why it’s important to find an ETL platform that does not require heavy set-up or extensive maintenance help from engineers.
Connect to Your Sources
Finally, it’s important to find an ETL tool that can connect to all of the sources that you use or that you could potentially need in the future. Preventing roadblocks in this area and maintaining a unified infrastructure can help prevent integration failures and improve your long-term success as you continue on your data journey.
The biggest takeaway? You have to start with a comprehensive understanding of your business and your needs. Once your ETL is established, you’ll be able to focus on visualizing your data to drive key business decisions and unlock valuable insights.