ETL is an acronym for Extract, Transform and Load. It refers to a process of extracting data from one system and transforming it so that it can be loaded into another system. It is the process that lets businesses amass large amounts of data in data warehouses that they can use for business operations. Reverse ETL is a term used when the data is pushed from the warehouse to the CRM. This process seems simple, but there are many pitfalls that can come up during this phase.

Azure Synapse Analytics, still commonly known as Azure Data Warehouse, is Microsoft's cloud data warehouse that processes relational and non-relational data for analytics. As one of the most popular data warehousing tools, Azure lets you generate real-time insights into almost every aspect of your business, from sales to customer service. But how do you get data to Azure in the first place? That's where an Extract, Transform, and Load (ETL) tool proves useful.

Salesforce is the world’s leading CRM (customer relationship management) software, with a 20 percent market share. The Salesforce CRM software is chock-full of features for business intelligence (BI) and analytics so that you can capture hidden insights and make smarter, data-driven decisions. The traditional ETL (extract, transform, load) process extracts data from one or more sources and then deposits it into a centralized data repository.

Choosing the right data warehouse is a critical component of your general data and analytic business needs. One of the biggest questions that businesses ask when choosing their data warehouse providers is this: Should you use Snowflake, Amazon RedShift, or Google's BigQuery data warehouse for your business needs? We've already covered Amazon RedShift vs. Snowflake and Google BigQuery vs. Snowflake, but what about Amazon RedShift vs. Google BigQuery?

Data warehouses help you run logical queries, build accurate forecasting models, and identify impactful trends throughout your organization. But, what goes into designing a data warehouse? Whether you choose to utilize a pre-built vendor solution or you're starting from scratch — you'll need some level of warehouse design to successfully adopt a new data warehouse.