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Data Integrations in Big Data

Data Integrations in Big Data

$298.00
How you learn:
Self-paced • Lifetime updates
When you get access:
Course access is prepared after purchase and delivered via email
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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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What does the Data Integrations in Big Data course cover?

Data Integrations in Big Data is covered here in 9 modules: Architecting Scalable Data Ingestion Pipelines, Data Source Connectivity and Protocol Management, Schema Evolution and Data Modeling and 6 more. The outline lists 72 specific topics, opening with selecting between batch and streaming ingestion based on SLA requirements and source system capabilities and closing with simulating failure scenarios (e.g., source outage, schema.

How do you approach Data Integrations in Big Data step by step?

The work is sequenced in 9 stages. It starts with Architecting Scalable Data Ingestion Pipelines, moves through Data Source Connectivity and Protocol Management and Schema Evolution and Data Modeling, and ends at Monitoring, Alerting, and Incident Response. Each stage carries its own topic list, so the sequence is followed rather than summarised.

What is in Module 1 of the Data Integrations in Big Data course?

Module 1 is Architecting Scalable Data Ingestion Pipelines. It works through selecting between batch and streaming ingestion based on SLA requirements and source system capabilities, designing idempotent ingestion workflows to handle duplicate messages in distributed systems, implementing backpressure mechanisms in Kafka consumers to prevent downstream system overload and 5 more. It sets the vocabulary the remaining 8 modules build on.

How is the Data Integrations in Big Data course delivered?

The Data Integrations 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 Data Integrations in Big Data course cost?

The Data Integrations 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: Data Integrity in Big Data, Big Data Integration in Data integration Dataset, Data Governance Integration in Big Data, Big Data in Data integration Dataset.

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

This curriculum spans the technical and operational complexity of a multi-workshop program focused on building and operating enterprise-grade data integration systems, comparable to the internal capability programs used to scale data platforms across large organisations.

Module 1: Architecting Scalable Data Ingestion Pipelines

  • Selecting between batch and streaming ingestion based on SLA requirements and source system capabilities
  • Designing idempotent ingestion workflows to handle duplicate messages in distributed systems
  • Implementing backpressure mechanisms in Kafka consumers to prevent downstream system overload
  • Configuring retry strategies with exponential backoff for transient failures in cloud-based APIs
  • Partitioning strategies for large-scale file ingestion from object storage to optimize parallel processing
  • Validating data schema upon ingestion using schema registry integration with Avro or Protobuf
  • Monitoring end-to-end latency across ingestion stages using distributed tracing tools like OpenTelemetry
  • Securing data in transit using mTLS and enforcing authentication between ingestion components

Module 2: Data Source Connectivity and Protocol Management

  • Choosing appropriate JDBC fetch sizes and connection pooling parameters for high-volume RDBMS extraction
  • Implementing OAuth2.0 and API key rotation for secure access to SaaS platforms like Salesforce or Workday
  • Handling change data capture (CDC) from PostgreSQL logical replication slots without causing WAL bloat
  • Configuring gRPC endpoints for efficient communication with microservices exposing real-time data
  • Managing API rate limits and quotas when extracting from third-party RESTful services
  • Integrating mainframe data via MQ series with proper message segmentation and recovery logic
  • Using SSH tunneling for secure access to on-premises databases in hybrid cloud environments
  • Normalizing data from heterogeneous sources with inconsistent timestamp formats and encodings

Module 3: Schema Evolution and Data Modeling

  • Defining backward- and forward-compatible schema changes in a schema registry for Avro-based pipelines
  • Mapping nested JSON structures from application logs into flattened Parquet schemas for analytics
  • Implementing slowly changing dimensions (SCD Type 2) in data lakehouse environments using Delta Lake
  • Resolving naming conflicts and semantic mismatches across departments during enterprise data modeling
  • Enforcing data type consistency when merging datasets from sources with implicit typing (e.g., CSV)
  • Designing partitioning and bucketing strategies in Hive-compatible metastores for query performance
  • Managing schema drift detection using statistical profiling and automated alerting
  • Versioning data models in source control and coordinating deployment across staging and production

Module 4: Data Quality and Validation Frameworks

  • Embedding Great Expectations or Soda Core checks into ingestion DAGs for early failure detection
  • Configuring nullability rules per field based on business-criticality and downstream dependencies
  • Implementing referential integrity checks across distributed datasets without foreign key constraints
  • Quantifying data completeness by comparing record counts against upstream source audit logs
  • Setting thresholds for anomaly detection on data distributions using historical baselines
  • Logging data quality violations to a centralized observability platform for root cause analysis
  • Handling quarantine workflows for invalid records with manual review and reprocessing paths
  • Defining SLAs for data freshness and accuracy in service-level agreements with data consumers

Module 5: Metadata Management and Lineage Tracking

  • Instrumenting pipeline steps to emit operational metadata (e.g., row counts, timestamps) to a metadata store
  • Integrating Apache Atlas or Marquez for end-to-end lineage across batch and streaming jobs
  • Automating schema documentation extraction and publishing to a data catalog on pipeline deployment
  • Mapping business glossary terms to technical column names using a stewardship workflow
  • Tracking data ownership and PII classification tags through automated scanning and manual override
  • Implementing impact analysis features to assess downstream effects of source schema changes
  • Enabling search and discovery of datasets using full-text and faceted search in a catalog UI
  • Archiving and versioning metadata snapshots to support audit and compliance requirements

Module 6: Security, Privacy, and Access Governance

  • Implementing row- and column-level security in query engines like Presto or Spark SQL
  • Masking PII fields dynamically using deterministic encryption or hashing in reporting layers
  • Integrating with enterprise IAM systems (e.g., Okta, Azure AD) for attribute-based access control
  • Auditing access patterns to sensitive datasets using query log monitoring and alerting
  • Applying data retention policies based on legal hold requirements and GDPR right-to-erasure
  • Encrypting data at rest using customer-managed keys in cloud storage services
  • Conducting data protection impact assessments (DPIAs) for new high-risk data integrations
  • Enforcing secure handoffs between teams using signed data release manifests

Module 7: Orchestration and Pipeline Operations

  • Designing Airflow DAGs with proper task dependencies, retries, and SLA monitoring
  • Parameterizing pipelines to support multi-environment deployment (dev, test, prod) without code changes
  • Implementing pipeline idempotency to allow safe reruns after partial failures
  • Managing cross-DAG dependencies using external task sensors or message-based triggers
  • Scaling executor types (Kubernetes, Celery) based on workload concurrency and resource demands
  • Integrating pipeline logs with centralized logging systems (e.g., ELK, Datadog) for troubleshooting
  • Scheduling backfills with controlled concurrency to avoid resource contention
  • Automating pipeline testing using synthetic datasets and mock sources in CI/CD pipelines

Module 8: Performance Optimization and Cost Control

  • Tuning Spark executors (memory, cores, instances) based on data volume and shuffle behavior
  • Optimizing file sizes in data lakes to balance query performance and storage costs
  • Implementing predicate pushdown and column pruning in source connectors for efficient filtering
  • Choosing between format options (Parquet, ORC, Iceberg) based on update patterns and query workloads
  • Monitoring cloud data transfer costs and minimizing cross-region egress in hybrid architectures
  • Using materialized views or pre-aggregated tables to accelerate frequent analytical queries
  • Right-sizing cluster nodes in managed services (e.g., EMR, Databricks) to match workload profiles
  • Implementing auto-scaling policies for streaming consumers based on lag metrics

Module 9: Monitoring, Alerting, and Incident Response

  • Defining critical metrics (e.g., lag, throughput, error rate) for real-time pipeline dashboards
  • Setting dynamic alert thresholds using statistical baselines instead of static values
  • Correlating pipeline failures with infrastructure metrics (CPU, memory, network) for root cause analysis
  • Implementing health checks for dependent services to detect cascading failures early
  • Creating runbooks with step-by-step recovery procedures for common failure scenarios
  • Automating rollback of pipeline deployments using versioned artifacts and infrastructure as code
  • Conducting blameless postmortems and tracking remediation tasks in a centralized system
  • Simulating failure scenarios (e.g., source outage, schema change) in staging for resilience testing
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