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Initialization involves setting up the execution environment for your data processing tasks. This step includes:
Cluster Initialization: Spinning up a compute cluster (if not already active) to execute your pipeline.
Loading Dependencies: Loading libraries, configurations, and other dependencies needed for data processing.
Setting Up Context: Establishing connections to data sources, defining schemas, and initializing variables.
Think of it as preparing the workspace before actual data processing begins.
Setting Up Tables:
This step focuses on creating or configuring tables, views, or data structures where your processed data will reside.
It includes:
Schema Definition: Creating tables with appropriate column names, data types, and constraints.
Partitioning: Designing how data is partitioned (e.g., by date, region, or other relevant attributes).
Indexing: Setting up indexes for efficient querying.
Data Loading: Populating tables with data from various sources (e.g., files, databases, streams).
Essentially, it’s about organizing and preparing the storage layer for your data.
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