Data Processing

Transforming raw data into valuable insights

🏭 Analogy

The factory that refines raw materials into finished products

Problems Solved

  • Data transformation and enrichment
  • Aggregation and summarization
  • Business logic implementation
  • Performance optimization

Understanding Data Processing

Types of Data Processing

Batch Processing (ETL/ELT)

Processing large datasets in scheduled batches

Examples: Nightly data warehouse loads, hourly aggregations, daily reports
Best for: Large datasets, cost optimization, complex transformations

Stream Processing

Real-time processing of continuous data flows

Examples: Live analytics, fraud detection, IoT sensor processing
Best for: Real-time insights, immediate response, event-driven systems

Machine Learning Pipelines

Automated model training and inference on data

Examples: Recommendation systems, predictive analytics, anomaly detection
Best for: Pattern recognition, automation, predictive insights

Data Validation & Cleaning

Ensuring data quality and consistency

Examples: Schema validation, duplicate detection, data profiling
Best for: Data governance, quality assurance, reliable analytics

Recommended Tools

Tools for data processing by category:

Pinecone

Use case: AI embeddings, semantic search

Cloud Service Vector Database
When to use: AI embeddings, semantic search

Weaviate

Use case: Knowledge graphs, semantic search

Open Source Vector Database
When to use: Knowledge graphs, semantic search

Feast

Use case: ML feature management

Open Source Feature Store
When to use: ML feature management

MLflow

Use case: ML experiment tracking

Open Source ML Platform
When to use: ML experiment tracking

Kubeflow

Use case: ML pipeline orchestration

Open Source ML Platform
When to use: ML pipeline orchestration

Apache Flink

Use case: Real-time analytics, low-latency

Distributed Stream Processing
When to use: Real-time analytics, low-latency

Monte Carlo

Use case: Data quality monitoring

Cloud Service Data Observability
When to use: Data quality monitoring

Great Expectations

Use case: Data validation, testing

Open Source Data Testing
When to use: Data validation, testing

AWS Glue

Use case: Serverless ETL, data catalog

Serverless ETL
When to use: Serverless ETL, data catalog

Google Dataflow

Use case: Stream and batch processing

Serverless Stream Processing
When to use: Stream and batch processing

Azure Databricks

Use case: Big data analytics, ML

Analytics Distributed Computing
When to use: Big data analytics, ML

Databricks

Use case: Big data, ML workflows

Analytics Distributed Computing
When to use: Big data, ML workflows

Apache Spark

Use case: Big data transformations

Batch + Streaming Distributed Computing
When to use: Big data transformations

Apache Flink

Use case: Real-time analytics, low-latency

Streaming Stream Processing
When to use: Real-time analytics, low-latency

dbt

Use case: Data warehouse transforms

SQL-first Transformation
When to use: Data warehouse transforms

Pandas

Use case: Data analysis, cleaning

In-memory Python Library
When to use: Data analysis, cleaning

Great Expectations

Use case: Data quality testing

Validation Data Quality
When to use: Data quality testing

Next Steps

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