Skip to main content
Big data processing in Big Data

Big data processing in Big Data

$298.00
How you learn:
Self-paced • Lifetime updates
Who trusts this:
Trusted by professionals in 160+ countries
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.
When you get access:
Course access is prepared after purchase and delivered via email
Your guarantee:
30-day money-back guarantee — no questions asked
Adding to cart… The item has been added

What does the Big data processing in Big Data course cover?

Big data processing in Big Data is covered here in 9 modules: Architecting Scalable Data Ingestion Pipelines, Distributed Storage Design and Optimization, Large-Scale Data Processing with Distributed Engines and 6 more. The outline lists 72 specific topics, opening with designing idempotent ingestion workflows to handle duplicate messages from high-throughput sources like Kafka or Kinesis and closing with supporting self-service discovery with full-text.

How do you approach Big data processing in Big Data step by step?

The work is sequenced in 9 stages. It starts with Architecting Scalable Data Ingestion Pipelines, moves through Distributed Storage Design and Optimization and Large-Scale Data Processing with Distributed Engines, and ends at Data Governance and Metadata Management. Each stage carries its own topic list, so the sequence is followed rather than summarised.

What is in Module 1 of the Big data processing in Big Data course?

Module 1 is Architecting Scalable Data Ingestion Pipelines. It works through designing idempotent ingestion workflows to handle duplicate messages from high-throughput sources like Kafka or Kinesis, selecting between batch and streaming ingestion based on SLA requirements and source system capabilities, implementing backpressure mechanisms in Spark Streaming or Flink to prevent consumer overload during traffic spikes and 5 more.

How is the Big data processing in Big Data course delivered?

The Big data processing 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 Big data processing in Big Data course cost?

The Big data processing in Big Data course is $302 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: Archival processes in Big Data, Process Modelling in Big Data, Digital Processes in Big Data, Process Management in Big Data.

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

This curriculum spans the design and operational rigor of a multi-workshop technical enablement program for data engineering teams, covering the breadth of decisions and trade-offs involved in building and maintaining enterprise-scale data platforms.

Module 1: Architecting Scalable Data Ingestion Pipelines

  • Designing idempotent ingestion workflows to handle duplicate messages from high-throughput sources like Kafka or Kinesis
  • Selecting between batch and streaming ingestion based on SLA requirements and source system capabilities
  • Implementing backpressure mechanisms in Spark Streaming or Flink to prevent consumer overload during traffic spikes
  • Configuring secure, authenticated data transfer from on-premises databases to cloud data lakes using encrypted tunnels
  • Choosing appropriate serialization formats (Avro vs. JSON vs. Protobuf) based on schema evolution and parsing performance
  • Partitioning strategies for ingested data to optimize downstream query performance in distributed storage systems
  • Monitoring data freshness and latency across ingestion stages using custom metrics and alerting thresholds
  • Handling schema drift in semi-structured data by implementing schema validation and auto-registration in a schema registry

Module 2: Distributed Storage Design and Optimization

  • Selecting file formats (Parquet, ORC, Delta Lake) based on query patterns, ACID requirements, and compute engine compatibility
  • Implementing partitioning and bucketing strategies in Hive-style tables to reduce scan overhead in petabyte-scale datasets
  • Designing lifecycle policies for tiered storage (hot, cold, archive) using S3 Intelligent Tiering or equivalent cloud services
  • Configuring replication and erasure coding in HDFS or object storage to balance durability and storage cost
  • Enabling compression algorithms (Snappy, Zstandard) based on CPU overhead and I/O reduction trade-offs
  • Implementing column-level encryption or field masking for sensitive data at rest in shared storage environments
  • Validating data integrity using checksums and manifest files after large-scale ETL operations
  • Managing metadata consistency in distributed file systems during concurrent write operations from multiple clusters

Module 3: Large-Scale Data Processing with Distributed Engines

  • Tuning Spark executor memory and core allocation to minimize garbage collection and maximize parallelism
  • Optimizing shuffle behavior by adjusting partition counts and enabling shuffle service in YARN or Kubernetes
  • Choosing between DataFrame API and RDD based on optimization needs and debugging complexity
  • Implementing broadcast joins for small dimension tables to reduce shuffle overhead in Spark SQL
  • Configuring speculative execution to mitigate straggler tasks in heterogeneous cluster environments
  • Debugging stage failures using Spark UI metrics, GC logs, and event log analysis
  • Managing dynamic resource allocation to scale cluster size based on workload demand
  • Integrating custom UDFs with type safety and performance considerations in PySpark or Scala

Module 4: Real-Time Stream Processing Architecture

  • Designing event-time processing with watermarks to handle late-arriving data in Flink or Spark Structured Streaming
  • Implementing exactly-once semantics using checkpointing and two-phase commit in sink operations
  • Choosing windowing strategies (tumbling, sliding, session) based on business SLAs and data arrival patterns
  • Scaling stateful stream applications by tuning key-by partitioning and managing state backend (RocksDB vs. heap)
  • Integrating stream processing jobs with external systems (databases, caches) using async I/O to avoid blocking
  • Monitoring processing lag and throughput to detect consumer degradation in real-time pipelines
  • Handling schema evolution in streaming data by integrating schema registry with deserialization logic
  • Isolating and testing state recovery behavior during controlled job restarts and failures

Module 5: Data Quality and Observability at Scale

  • Implementing schema conformance checks at ingestion using Deequ or Great Expectations in batch pipelines
  • Designing statistical profiling jobs to detect anomalies in data distributions over time
  • Setting up automated alerting for null rate thresholds, value range violations, or unexpected cardinality shifts
  • Integrating data lineage tracking with metadata stores to trace root causes of quality issues
  • Building reconciliation reports between source and target systems to validate ETL accuracy
  • Instrumenting pipelines with custom metrics exported to Prometheus or Datadog for operational visibility
  • Creating synthetic data generators to simulate edge cases in testing environments
  • Managing data quality rule lifecycle across development, staging, and production environments

Module 6: Security, Access Control, and Compliance

  • Implementing fine-grained access control in data lakes using Apache Ranger or AWS Lake Formation policies
  • Enforcing column- and row-level security in SQL engines like Presto or Spark with dynamic filtering
  • Integrating Kerberos or OAuth2 for secure authentication in multi-tenant cluster environments
  • Masking PII fields in query results using UDFs or view-layer transformations for non-privileged roles
  • Auditing data access patterns and query logs to meet regulatory requirements like GDPR or HIPAA
  • Rotating encryption keys and credentials using secret management tools (Hashicorp Vault, AWS Secrets Manager)
  • Validating end-to-end encryption in transit for data moving between services using mTLS
  • Conducting periodic access reviews and privilege revocation for stale service accounts

Module 7: Orchestration and Workflow Management

  • Designing DAGs in Airflow with appropriate task boundaries and retry strategies for idempotency
  • Managing cross-DAG dependencies using external task sensors or message-based triggers
  • Securing Airflow metadata database and web server with role-based access and network isolation
  • Parameterizing workflows to support multi-environment deployment (dev, staging, prod) without code duplication
  • Implementing SLA monitoring and failure notifications using custom callbacks and alert integrations
  • Scaling Airflow components (workers, schedulers) using Kubernetes operators for high availability
  • Version-controlling DAG code and managing deployment via CI/CD pipelines with rollback capability
  • Handling long-running tasks by delegating to external systems and polling for completion

Module 8: Performance Tuning and Cost Optimization

  • Right-sizing cluster configurations based on historical utilization metrics and workload patterns
  • Implementing spot instance usage with checkpointing to reduce compute costs in cloud environments
  • Optimizing query performance through predicate pushdown, column pruning, and indexing strategies
  • Consolidating small files in object storage using compaction jobs to improve read efficiency
  • Using materialized views or pre-aggregated tables to accelerate frequent analytical queries
  • Monitoring and eliminating resource contention in shared clusters using queue management in YARN
  • Conducting cost attribution by tagging jobs with project, team, or cost center identifiers
  • Implementing auto-scaling policies based on queue depth or CPU/memory utilization thresholds

Module 9: Data Governance and Metadata Management

  • Deploying a centralized metadata catalog (Apache Atlas, AWS Glue Data Catalog) for discoverability
  • Automating metadata extraction from ETL jobs and registering it with lineage context
  • Enforcing data ownership and stewardship assignments for critical datasets
  • Implementing data classification tags (PII, financial, internal) for policy enforcement
  • Integrating business glossary terms with technical metadata to bridge domain understanding
  • Managing dataset deprecation and archival workflows with stakeholder notifications
  • Validating metadata consistency across systems (warehouse, BI tools, data science platforms)
  • Supporting self-service discovery with full-text search, facet filtering, and usage statistics
!