Sound business intelligence (BI) requires loading data from multiple sources to a single destination from which they can be correlated and analyzed.
For online retailer Bonami, getting data from a MySQL database to a Google BigQuery data warehouse required a powerful data pipeline — a challenge many businesses face. By using an ETL tool instead of writing their own solution, Bonami was able to begin replicating its MySQL data in a matter of minutes.
MySQL databases hold critical insights for your business
A popular open source relational database management system (RDBMS), MySQL scales both horizontally and vertically, making it great for browser-based applications and online use cases. It runs on virtually any operating system, is designed to store large volumes of data, and serves as an excellent transactional database.
Yet despite the fact that MySQL is one of the most widely deployed databases, it’s not an optimal platform for advanced analytics. MySQL’s architecture is ideal for online transaction processing (OLTP) systems, for which data — individual records such as customers, accounts, or sessions — is best stored by rows. Most OLTP systems aren’t designed for the massive, complex queries that drive BI, or analytics across different business categories of data or extensive time periods, such as quarterly reports.
Data for online analytical processing (OLAP) systems — measurements that comprise large groups of records — is best stored by columns. Since MySQL uses row-oriented storage, producing data analytics requires replicating MySQL data (along with data from other sources) into a column-oriented data warehouse, where it can be queried for BI. However, writing data pipeline code can be time-consuming and resource-intensive.
How ETL readies MySQL data for analytics
Here’s a brief overview of how a data pipeline, also called an ETL process, works:
- Extract: Extraction involves taking data out of MySQL.
- Transform: During transformation, the MySQL data is adjusted to the schema of the target system.
- Load: The transformed MySQL data is loaded into the target system.
Implementing this process was Bonami’s critical challenge. Rather than write their own code, they decided that an ETL tool was the best way to simplify the process. But whatever tool they chose had to address several challenges any organization faces when moving MySQL data to a data warehouse.
Moving MySQL data to the warehouse
It takes time to replicate data from extremely large MySQL data stores. Processor load and network traffic issues can negatively affect the applications using MySQL, which in turn can cause application consistency issues.
To ensure their data pipeline performs optimally, organizations must plan for several challenges when moving MySQL data to a data warehouse:
- Schema management is an issue when a database supports data types that aren’t available in the data warehouse to which the data is being replicated. Or, the source database might support nested data structures, but the data warehouse won’t. In such a scenario, the ETL process must perform data type conversion or de-nest the source data structure before loading it.
- Data integration requires programmers with knowledge of both the source and the target system’s APIs and code.
- Network connections may become strained. By distributing MySQL nodes to handle processing of big data-sized workloads, organizations may put a strain on their networks.
(Want a deeper dive into the technical issues involved in replicating MySQL data to your warehouse? We’ve got you covered.)
A surefire way to expedite MySQL data replication
By eliminating the need to write code for extracting, transforming, and loading MySQL data, an ETL platform like Stitch offers faster time to value for a data pipeline. Consequently, organizations can focus on the business value of integrations, instead of constantly configuring and reconfiguring them. Data professionals can set up and begin moving data in just minutes, and can automate replication for selected data at user-defined intervals.
The latest generation of cloud-native data warehouses can scale elastically to handle transformation processing using the same hardware on which the data warehouse runs. That means data engineers no longer have to define transformations within the data pipeline.
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For businesses that use cloud data warehouses like Amazon Redshift, Google BigQuery, Snowflake, and Microsoft Azure SQL Data Warehouse, conventional ETL has become ELT. Data scientists can run transformations on the data in the data warehouse, providing flexibility to transform the data differently as needed.
This approach worked well for Bonami. Within two weeks of signing up for Stitch, the company had all of its historical data uploaded to Google BigQuery, and today, Bonami analyzes its MySQL data in a modern cloud warehouse with cutting-edge BI technologies.
Using Stitch saved the company 10 working days of developer time.
Make the most of MySQL with Stitch
With connections to the most widely used SaaS applications and data warehouses, Stitch empowers business users and IT teams to glean meaningful insights from data.
Stitch reduces both the time and spend it takes to implement MySQL replication, and empowers businesses to extract data from more than 90 other data sources and move it to popular cloud data warehouses.
The Stitch solution is simple, straightforward, and ready to implement immediately. Why wait? Set up a free trial in minutes and begin reaping vital insights from your MySQL data.