ETL (Extract, Transform, Load)
Definition: ETL is a traditional data workflow where you extract data from one or more sources, transform it to fit analytical needs, and load it into a target database or warehouse.
Why It Matters: ETL ensures your analytics and reporting systems receive clean, structured, and ready-to-use data.
Common Tools: Apache Spark, Talend, dbt, Python (Pandas), Apache NiFi.
Pitfall: Long transformations can slow down the process — design for idempotency so retries don’t cause duplicates.
ELT (Extract, Load, Transform)
Definition: In ELT, raw data is first loaded into a storage system (like a data lake) and then transformed there.
Why It Matters: Modern data warehouses and lakes are powerful enough to handle transformations internally, reducing data movement.
Common Tools: Snowflake, BigQuery, dbt, Spark SQL.
Pitfall: Maintain separate layers for raw and curated data — mixing them can lead to confusion and errors.
Data Lake
Definition: A centralized repository for storing raw, unprocessed data in its native format.
Why It Matters: Data lakes can store massive volumes of structured, semi-structured, and unstructured data cost-effectively.
Common Tools: Amazon S3, Azure Data Lake, Google Cloud Storage, MinIO.
Pitfall: Without governance, a data lake can quickly turn into a data swamp — establish folder structures and metadata rules early.
Data Warehouse
Definition: A structured, optimized system for analytical queries.
Why It Matters: Warehouses store clean, processed data for business intelligence and reporting.
Common Tools: Snowflake, Redshift, BigQuery, PostgreSQL.
Pitfall: Ensure proper schema design (star/snowflake) to avoid performance bottlenecks.
Lakehouse
Definition: A hybrid architecture combining the scalability of a data lake with the performance and structure of a data warehouse.
Why It Matters: Offers ACID transactions, time travel, and schema enforcement without leaving the data lake.
Common Tools: Delta Lake, Apache Iceberg, Apache Hudi.
Pitfall: Choosing the right table format early is critical migrating later can be costly.
Data Pipeline
Definition: An automated sequence of processes that moves and transforms data from sources to destinations.
Why It Matters: Pipelines make data workflows repeatable, reliable, and scalable.
Common Tools: Kafka, Spark, Flink, Airflow, Prefect.
Pitfall: Build with observability in mind — add logging, metrics, and retries.
Batch Processing
Definition: Data is collected and processed in bulk at scheduled intervals.
Why It Matters: Simple and efficient for jobs that aren’t time-sensitive (e.g., daily reports).
Common Tools: Spark batch jobs, Airflow, cron jobs.
Pitfall: Avoid overly large batches; they can fail and take hours to reprocess.
Stream Processing
Processing data as it arrives, enabling real-time analytics and decision-making.
Common use cases include fraud detection, live leaderboards, and IoT telemetry.
Technologies: Apache Kafka, Spark Structured Streaming, Apache Flink.
Change Data Capture (CDC)
A method of tracking inserts, updates, and deletes in a database and propagating those changes downstream.
It’s critical for keeping systems synchronized without constantly reloading entire datasets.
Example: Debezium for capturing changes from PostgreSQL or MySQL into Kafka.
Data Modeling
The art of structuring data so it’s easy to query, maintain, and extend.
Two common styles:
OLTP models (normalized) for transaction systems.
OLAP models (dimensional) for analytics, often in star or snowflake schemas.
Good modeling improves performance, usability, and maintainability.
Physical Data Layout
How data is stored on disk has huge performance implications.
Key decisions:
File format: Parquet, ORC (columnar, compressed) vs JSON/CSV (flexible but heavy).
Compression: Snappy, ZSTD, Gzip.
Partitioning: Organizing data by date, region, or other keys to reduce scan time.
Poor layout can lead to small-file problems or expensive queries.
Orchestration & Scheduling
The process of coordinating tasks so they run in the right order, with retries, alerts, and dependencies handled.
Orchestration ensures that if one stage fails, downstream jobs are paused or retried.
Tools: Apache Airflow, Prefect, Dagster.
Data Quality & Testing
Garbage in = garbage out.
Data quality checks ensure accuracy, completeness, and consistency before data is used.
Common checks: null values, duplicates, range violations, schema mismatches.
Tools: Great Expectations, Soda Core, dbt tests.
Metadata, Catalog & Lineage
Metadata describes your datasets (schema, owner, freshness).
A data catalog makes it easy for teams to find the right data.
Data lineage shows how data moves and transforms across systems, helping with debugging and compliance.
Popular tools: DataHub, OpenMetadata, Amundsen.
Governance, Security & Privacy
Policies and tools to ensure data is used securely and ethically.
This includes:
Access controls (RBAC, ABAC).
Encryption (in transit and at rest).
Data masking/tokenization for sensitive fields.
Compliance with laws like GDPR and HIPAA.
Good governance isn’t just compliance—it builds trust in your data platform.
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