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7 Things to Ask from a Data Loader

The emergence of cloud data warehouses (CDW) has transformed the way data is prepared for analytics. CDWs have significantly reduced the barriers to entry in making data-driven business decisions, even for the smallest of organizations. As CDW adoption has grown, IT, as well as business teams, are looking for ways to load data quickly into cloud data warehouses and accelerate analytics. An Extract Load Transform (ELT) architecture addresses this market demand.

Data loaders solve the first two parts of the ELT architecture. Data loaders ‘extract’ data from applications and data sources and then ‘load’ it into the cloud data warehouse. Data loaders help get data swiftly into the CDW so that it’s available for transformations within the data warehouse and downstream analytics sooner.

There are several tools in the market that can load data into a CDW. As you look to identify a data loader that will work best for you, here is a helpful list of 7 key things you should consider.

  • Can it load data into your favorite CDW, be it Snowflake, Redshift?

This is an obvious one but a natural place to start. Can the data loader load data into your favorite CDW? If not, that is a deal-breaker.  

  • How quickly can you load the entire source database into the cloud data warehouse?

How fast is the data loader? Does it load 500 GB / 5 TB worth of tables into the CDW in hours? Or does it take days? Because the performance of the data loader platform will dictate how frequently you can sync complete data sets such as getting data from your subsidiaries, regional warehouses, franchisee stores, etc.

  • Can it effectively handle changes to the source schema? 

Schema (table and column definitions) or object fields of the source systems change frequently. Can the data loader automatically handle such changes or does it require human intervention every time? A tool that can handle schema changes automatically will significantly reduce the maintenance time and improve pipeline robustness.

  • Does the data loader provide built-in support for capturing historical data (SCD2)? 

Does the data loader provide built-in support for cases when your column data changes? For example, if you are uploading customer information to the CDW, and if there was a change in Account Executives managing a customer account, will the data loader automatically preserve the historical data or get rid of it? Having a tool that automatically historizes data helps you gain insights that would otherwise be lost due to changing data.

  • Does the data loader respect the variety of data types (date, numeric, currency, etc.)?

Does the data loader respect all the different data types in the source system? Or does it convert every data type into a VARCHAR? The ability to maintain the integrity of data types in a schema is important to avoid any manual work once the data is in your CDW.

  • Do you need to know coding or even SQL to load data into the data warehouse? 

Source data sometimes needs to be modified before loading into the CDW e.g. tracking changing dimensions. Does the data loader provide an effective tool to do all that without writing code or SQL? With a no-code tool for data loading, even business users can use the data loader and reduce the burden on overstretched IT teams.

  • Does the data loader give you predictable pricing? 

Some data loaders are very attractive because you can start with just a credit card. Initially, this might seem like an affordable option because you are moving small amounts of data. But can you predict your spend six months to a year from now when your data loads have grown 10x or 50x? How much will it cost then? Will you be comfortable with that level of spend on that data loader? Will you even know if the amount of budget you allocated for the data loader will be enough? If you expect the amount of data you will analyze will grow exponentially, you need a data loader that can scale and still be affordable at the higher data volumes.

A fast and effective data loader is a key driver to organization-wide data-driven insights. As you evaluate your options, keep this checklist handy by downloading it here

Dhananjay Bapat

Dhananjay is a Senior Technical Product Marketing Manager at SnapLogic.


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