Data professionals can set up and begin moving data in just minutes, and can automate replication for selected data at user-defined intervals. Consequently, organizations can focus on the business value of integrations, instead of constantly configuring and reconfiguring them.
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(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 replicationīy 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. By distributing MySQL nodes to handle processing of big data-sized workloads, organizations may put a strain on their networks.
Network connections may become strained. Data integration requires programmers with knowledge of both the source and the target system’s APIs and code. In such a scenario, the ETL process must perform data type conversion or de-nest the source data structure before loading it. Or, the source database might support nested data structures, but the data warehouse won’t. 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. To ensure their data pipeline performs optimally, organizations must plan for several challenges when moving MySQL data to a data warehouse: Processor load and network traffic issues can negatively affect the applications using MySQL, which in turn can cause application consistency issues. It takes time to replicate data from extremely large MySQL data stores. But whatever tool they chose had to address several challenges any organization faces when moving MySQL data to a data warehouse. Rather than write their own code, they decided that an ETL tool was the best way to simplify the process. Implementing this process was Bonami’s critical challenge. Load: The transformed MySQL data is loaded into the target system. Transform: During transformation, the MySQL data is adjusted to the schema of the target system. Extract: Extraction involves taking data out of MySQL. Here’s a brief overview of how a data pipeline, also called an ETL process, works: However, writing data pipeline code can be time-consuming and resource-intensive. 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. 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.ĭata for online analytical processing (OLAP) systems - measurements that comprise large groups of records - is best stored by columns. 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. Yet despite the fact that MySQL is one of the most widely deployed databases, it’s not an optimal platform for advanced analytics. 
It runs on virtually any operating system, is designed to store large volumes of data, and serves as an excellent transactional database. MySQL databases hold critical insights for your businessĪ 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. 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. Sound business intelligence (BI) requires loading data from multiple sources to a single destination from which they can be correlated and analyzed.įor 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.
MySQL: Get the best insights from your data, faster than ever
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