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Improved Performance in Big Data

Improved Performance in Big Data

$298.00
How you learn:
Self-paced • Lifetime updates
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30-day money-back guarantee — no questions asked
Toolkit Included:
Includes a practical, ready-to-use toolkit containing implementation templates, worksheets, checklists, and decision-support materials used to accelerate real-world application and reduce setup time.
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Course access is prepared after purchase and delivered via email
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What does the Improved Performance in Big Data course cover?

Improved Performance in Big Data is covered here in 9 modules: Data Infrastructure Assessment and Readiness, Distributed Data Processing Optimization, Real-Time Stream Processing Architecture and 6 more. The outline lists 72 specific topics, opening with evaluate existing data pipeline latency to determine bottlenecks in ingestion from transactional databases to data lakes.

How do you approach Improved Performance in Big Data step by step?

The work is sequenced in 9 stages. It starts with Data Infrastructure Assessment and Readiness, moves through Distributed Data Processing Optimization and Real-Time Stream Processing Architecture, and ends at Cross-Platform Orchestration and DevOps. Each stage carries its own topic list, so the sequence is followed rather than summarised.

What is in Module 1 of the Improved Performance in Big Data course?

Module 1 is Data Infrastructure Assessment and Readiness. It works through evaluate existing data pipeline latency to determine bottlenecks in ingestion from transactional databases to data lakes., assess storage tiering strategies across hot, warm, and cold storage to balance cost and query performance., analyze schema evolution patterns in Parquet and Avro files to ensure backward compatibility in streaming environments. and 5 more.

How is the Improved Performance in Big Data course delivered?

The Improved Performance in Big Data course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.

How much does the Improved Performance in Big Data course cost?

The Improved Performance in Big Data course is $298 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.

Closely related courses: Performance Alignment in Big Data, Competitor financial performance in Big Data, Key Performance Indicators in Big Data, Big Data and E-Commerce Analytics, How to Use Data.

More answers: what you get with every course, refund policy, all help answers.

This curriculum spans the technical and operational rigor of a multi-workshop program focused on enterprise data platform modernization, covering the same breadth and depth as an internal capability build for end-to-end data engineering, governance, and performance optimization across hybrid and cloud environments.

Module 1: Data Infrastructure Assessment and Readiness

  • Evaluate existing data pipeline latency to determine bottlenecks in ingestion from transactional databases to data lakes.
  • Assess storage tiering strategies across hot, warm, and cold storage to balance cost and query performance.
  • Analyze schema evolution patterns in Parquet and Avro files to ensure backward compatibility in streaming environments.
  • Compare on-premises Hadoop clusters versus cloud data platforms (e.g., Databricks, BigQuery) based on data gravity and egress costs.
  • Validate data lineage tracking mechanisms to support auditability and impact analysis during schema changes.
  • Configure network bandwidth allocation between analytics workloads and production systems to prevent resource contention.
  • Implement data freshness SLAs by measuring end-to-end latency from source to reporting layer.
  • Document metadata inventory completeness, including field definitions, ownership, and PII classification.

Module 2: Distributed Data Processing Optimization

  • Tune Spark executor memory and core allocation to minimize garbage collection pauses in long-running jobs.
  • Partition large datasets by business key and time to reduce shuffle operations during joins.
  • Implement predicate pushdown and column pruning in query engines to limit data scanned from storage.
  • Convert broadcast joins to shuffled joins when size thresholds exceed cluster memory limits.
  • Monitor speculative execution behavior to identify straggler tasks in heterogeneous clusters.
  • Use dynamic allocation to scale executors based on queue depth in shared resource pools.
  • Optimize file size on object storage to balance metadata overhead and read parallelism (e.g., 128MB–1GB per file).
  • Profile CPU and I/O utilization across worker nodes to detect hardware imbalances in managed clusters.

Module 3: Real-Time Stream Processing Architecture

  • Choose between Kafka Streams and Flink based on stateful processing requirements and exactly-once semantics needs.
  • Design event-time windows with allowed lateness to handle delayed data in financial reconciliation pipelines.
  • Implement watermark strategies to balance completeness and latency in aggregate computations.
  • Scale Kafka consumer groups to match partition count while avoiding consumer overload.
  • Configure checkpoint intervals in Flink to minimize recovery time without degrading throughput.
  • Deploy stream processing jobs in high-availability mode with standby task managers.
  • Enforce schema validation at ingestion using Schema Registry to prevent malformed data propagation.
  • Isolate mission-critical streams from experimental pipelines using Kafka multi-tenancy.

Module 4: Data Quality and Observability Engineering

  • Define and automate threshold-based data quality checks (e.g., null rates, value distributions) in pipeline orchestration.
  • Instrument pipeline metrics collection using Prometheus exporters for custom data validation rules.
  • Integrate data profiling into CI/CD to detect schema drift before deployment to production.
  • Configure alerting on anomalous row counts or freshness delays using time-series anomaly detection.
  • Map data quality failures to downstream impact by linking datasets to business KPIs.
  • Implement quarantine zones for bad records with automated retry and escalation workflows.
  • Use statistical baselines to detect silent data corruption in slowly changing dimensions.
  • Log data validation outcomes to a centralized observability platform for audit trails.

Module 5: Scalable Data Modeling and Storage Design

  • Apply dimensional modeling techniques to create conformed dimensions across enterprise data marts.
  • Select between Delta Lake, Iceberg, and Hudi based on ACID requirements and cross-engine compatibility.
  • Implement slowly changing dimension strategies (Type 1, 2, 3) based on historical tracking needs.
  • Denormalize tables for analytical workloads while maintaining referential integrity through ETL logic.
  • Design zone-based data lake architecture (raw, curated, trusted) with access controls per zone.
  • Optimize indexing and clustering in cloud data warehouses (e.g., Snowflake clustering keys, Redshift sort keys).
  • Manage table lifecycle policies to archive or purge stale data based on regulatory requirements.
  • Version dataset schemas using Git and integrate with data catalog for change tracking.

Module 6: Performance Tuning in Cloud Data Warehouses

  • Size virtual warehouse instances based on query concurrency and memory-intensive operations.
  • Recluster tables after bulk updates to maintain sort key efficiency in columnar stores.
  • Convert large scans into materialized views or summary tables for frequent aggregations.
  • Use query profiling tools to identify high-cost operations like cross-joins or full table scans.
  • Implement workload management rules to isolate ETL, reporting, and ad hoc query queues.
  • Cache frequently accessed result sets using in-memory query acceleration layers.
  • Monitor credit consumption in serverless platforms to detect inefficient query patterns.
  • Apply data masking policies at query runtime to enforce row-level security.

Module 7: Data Governance and Access Control Implementation

  • Map data classification labels (e.g., PII, PCI) to automated masking and access policies.
  • Implement role-based access control (RBAC) aligned with organizational business units.
  • Integrate data catalog with IAM systems to synchronize user permissions across platforms.
  • Enforce data access auditing by capturing query logs and export actions in SIEM tools.
  • Define data stewardship roles and automate ownership assignment in metadata repositories.
  • Apply attribute-based access control (ABAC) for dynamic filtering based on user attributes.
  • Conduct quarterly access reviews to deprovision stale permissions in data systems.
  • Implement data usage agreements with legal teams for third-party data sharing.

Module 8: Machine Learning Pipeline Integration

  • Synchronize feature store refresh cycles with model retraining schedules to ensure consistency.
  • Version datasets used in model training to enable reproducible experiments.
  • Monitor feature drift by comparing statistical profiles between training and serving data.
  • Optimize batch scoring jobs using vectorized inference on GPU-enabled clusters.
  • Cache preprocessed features in Redis or Alluxio to reduce repeated computation.
  • Deploy shadow models to compare predictions before full cutover.
  • Log prediction outcomes and feedback signals for offline model evaluation.
  • Isolate training workloads from production inference using dedicated compute pools.

Module 9: Cross-Platform Orchestration and DevOps

  • Design DAGs in Airflow to handle inter-system dependencies between Spark, DBT, and ML jobs.
  • Parameterize pipeline templates to support multiple environments (dev, staging, prod) with configuration files.
  • Implement blue-green deployments for data pipelines to reduce rollback time during failures.
  • Use infrastructure-as-code (Terraform) to provision and version data platform components.
  • Integrate unit and integration tests into CI/CD for data transformation logic.
  • Encrypt secrets using Hashicorp Vault and inject them at pipeline runtime.
  • Standardize logging formats across tools to enable centralized log aggregation and search.
  • Enforce pipeline idempotency to allow safe reruns without data duplication.
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