Evolution of Data Pipelines
The role of data pipelines has evolved over three decades to support increasingly complex, cloud-driven data environments. This evolution comprises three phases:
- Phase 1: Load (1990-2010). During this phase companies used basic ETL pipelines that loaded periodic batches of database records, perhaps hourly, daily, or weekly, into a central data warehouse for business intelligence projects such as operational reporting and dashboards.
- Phase 2: Consolidate (2010s). In the second phase, companies modernized their environments by migrating analytics workloads to new data warehouses in the cloud. They sought to consolidate data from a rising number of sources, including IoT sensors, log files, and SaaS applications as well as traditional databases, into a data warehouse for BI or data lake for data science.
- Phase 3: Synchronize (2020s). Despite efforts to consolidate, data environments grow more diverse than ever. Companies maintain some data on premises due to regulatory concerns, data gravity, and the sheer cost of moving it all. While data warehouses and lakes start to merge into lakehouses, companies often have multiple such platforms across two or even three cloud providers. Data pipelines must synchronize data across these distributed elements in real time to support BI and AI/ML projects, as well as merged workflows in which analytical outputs trigger operational action.
Data pipeline management has evolved over three phases: from loading (1990-2010) to consolidation (2010s) to synchronization (2020s)