Best Practices

Best Practices for Designing Pipelines and Jobs

• Always implement robust auditing mechanisms for tracking data loads.

• Configure Python components to use a maximum of 1 core initially; scale based on monitoring

• Optimize memory allocation according to data volume and processing needs

• Allocate sufficient CPU and memory for Spark components based on the task complexity

• Minimize write operations to storage systems (e.g., Azure Blob Storage):

  • `Reduce write frequency

  • Merge records during ingestion to reduce I/O overhead

• Ensure that a recovery mechanism is in place to handle any failures or downtime effectively.

Best Practices for Ingesting Real-Time Data and Optimizing Data Processing in a Data Lake

Managing Small Files in Real-Time Ingestion

The Problem: Excessive Small Files

When streaming data (e.g., from Kafka, Kinesis, or Spark Structured Streaming) is written directly to a data lake, each micro-batch or task often creates its own file. Over time, this leads to millions of small files, which severely impact performance and stability.

Why do Small Files cause Problems?

• Performance degradation: Query engines must open thousands of files, creating excessive I/O overhead — the most expensive operation in distributed systems.

• Metadata overhead: Each file adds metadata entries in S3/ADLS, increasing both latency and cost.

• Job instability: Listing or scanning millions of files can exhaust driver memory or cause timeouts.

Best Practice: File Compaction

• Schedule periodic compaction jobs (e.g., hourly or daily) to merge small files into larger, optimally sized ones.

• This reduces I/O overhead, improves read performance, and accelerates downstream data processing.

Unified vs. Modular Data Processing Jobs

Unified (Single) Job Approach

Multiple aggregations or transformations are performed within one Spark job or pipeline — often from the same source DataFrame.

Benefits:

• Avoids repeated reads: The raw data is read once; multiple outputs share the same in-memory dataset, significantly reducing I/O.

• Optimizer reuse: Spark’s Catalyst optimizer can reuse common subplans (e.g., joins, filters), minimizing shuffle, CPU, and memory use.

• Reduced storage overhead: Intermediate data doesn’t need to be written back to S3/ADLS between stages.

• Consistency and atomicity: All aggregations are based on the same snapshot of data, ensuring consistent results across metrics.

Modular Job Approach

Each aggregation or transformation is implemented as a separate job, writing intermediate results between stages.

When to Use Modular Jobs:

• Resource constraints: Large unified jobs may require more memory or shuffle capacity than is available.

• Data reuse: Intermediate outputs can serve multiple downstream teams or pipelines.

• Parallel development: Different teams can own, deploy, and evolve their respective modules independently.

• Operational isolation: Easier to monitor, debug, and rerun individual stages if failures occur.

Collecting Data, Statistics, and Operational Metrics

When running a unified Spark job that performs multiple aggregations in one DAG, collecting inline data statistics and operational metrics provides strong observability, reliability, and optimization benefits.

Key Advantages

• Early detection of data quality issues: Track record counts, null ratios, schema drift, and min/max values in real time. Example: If the daily record count drops 90% or a column type changes, trigger an automated alert before the issue propagates.

• Reduced I/O and overhead: Gather all metrics directly from in-memory data without separate validation jobs or re-reads.

• Single source of truth for monitoring: Centralize runtime metrics (input volume, skewness, processing time) into one job → simpler dashboards and alerting.

• Performance optimization: Use statistics (e.g., partition size distribution, record counts) to detect skew and fine-tune partitioning, shuffle, and parallelism for future runs.

• Better resource utilization insights: Correlate Spark metrics (CPU, memory) with data-level metrics to identify performance bottlenecks and scaling needs.

• Faster debugging and RCA: Inline logs of record counts, schema summaries, and aggregation outputs simplify failure diagnosis — no need to re-run multiple jobs.

• Improved lineage and auditing: Capture key metadata per run:

  • Input/output record counts

  • Aggregation outputs

  • Job version and timestamps Example: Run ID 2025-11-07 processed 8.2M records → produced 5 KPIs with no data loss.

• Anomaly detection and alerting: Integrate metrics with Prometheus, Datadog, or CloudWatch to detect deviations (e.g., >20% drop in row count, schema change, performance regression).

• Continuous optimization and trend analysis: Aggregate metrics across runs to track long-term trends (data growth rate, column cardinality, storage footprint) for proactive tuning and capacity planning.