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Distributed Data in Big Data

Distributed Data in Big Data

$302.00
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What does the Distributed Data in Big Data course cover?

Distributed Data in Big Data is covered here in 9 modules: Architecting Scalable Data Ingestion Pipelines, Distributed Storage Systems and Data Layout, Distributed Query Processing and Optimization and 6 more. The outline lists 72 specific topics, opening with select and configure message brokers (e.g., Apache Kafka, Pulsar) based on throughput requirements, message durability, and replication needs across data centers.

How do you approach Distributed Data 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 Systems and Data Layout and Distributed Query Processing and Optimization, and ends at Cost Management and Resource Optimization. Each stage carries its own topic list, so the sequence is followed rather than summarised.

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

Module 1 is Architecting Scalable Data Ingestion Pipelines. It works through select and configure message brokers (e.g., Apache Kafka, Pulsar) based on throughput requirements, message durability, and replication needs across data centers., design partitioning strategies for Kafka topics to balance load and ensure even data distribution while avoiding hot partitions., implement idempotent producers and consumers to handle duplicate messages during retries in.

How is the Distributed Data in Big Data course delivered?

The Distributed Data 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 Distributed Data in Big Data course cost?

The Distributed Data 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: Distributed Ledger in Big Data, Big Data in Big Data, Distributed Data Systems Mastery across big data pipelines, Big Data Ethics in Big Data.

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 maintaining enterprise-scale data platforms, comparable to advisory engagements for designing resilient, governed, and cost-optimized big data architectures across distributed systems.

Module 1: Architecting Scalable Data Ingestion Pipelines

  • Select and configure message brokers (e.g., Apache Kafka, Pulsar) based on throughput requirements, message durability, and replication needs across data centers.
  • Design partitioning strategies for Kafka topics to balance load and ensure even data distribution while avoiding hot partitions.
  • Implement idempotent producers and consumers to handle duplicate messages during retries in high-availability scenarios.
  • Integrate schema registries (e.g., Confluent Schema Registry) to enforce schema evolution compatibility (backward, forward, full) across microservices.
  • Deploy change data capture (CDC) tools (e.g., Debezium) to stream database transactions into message queues with low latency and transactional consistency.
  • Configure backpressure handling in streaming pipelines to prevent consumer lag and system overload during traffic spikes.
  • Evaluate batch vs. streaming ingestion based on SLA requirements, data volume, and downstream processing complexity.
  • Secure data in transit using TLS and authenticate producers/consumers via SASL or mTLS in regulated environments.

Module 2: Distributed Storage Systems and Data Layout

  • Select file formats (Parquet, ORC, Avro) based on query patterns, compression efficiency, and schema evolution support in data lakes.
  • Implement partitioning and bucketing strategies in distributed file systems (e.g., HDFS, S3) to optimize query performance and reduce I/O overhead.
  • Configure replication factors and erasure coding in HDFS to balance fault tolerance and storage cost.
  • Design data lifecycle policies for tiered storage (hot, cold, archive) using S3 Intelligent-Tiering or similar services.
  • Implement metadata management using Hive Metastore or AWS Glue Catalog to enable cross-engine query compatibility.
  • Optimize data placement across regions and availability zones to meet data residency and low-latency access requirements.
  • Enforce immutable data writes and versioning in object stores to support auditability and point-in-time recovery.
  • Integrate with distributed caching layers (e.g., Alluxio) to accelerate access to frequently queried datasets.

Module 3: Distributed Query Processing and Optimization

  • Choose between MPP query engines (e.g., Presto, Trino, Spark SQL) based on concurrency needs, federation capabilities, and latency SLAs.
  • Tune query execution plans by adjusting shuffle partitions, broadcast join thresholds, and memory allocation per executor.
  • Implement cost-based optimization (CBO) using table statistics to improve join ordering and predicate pushdown.
  • Configure resource queues and workload management in shared clusters to isolate high-priority queries from ad hoc workloads.
  • Optimize data skipping using min/max statistics, Bloom filters, or Z-order indexing in columnar formats.
  • Enable predicate and projection pushdown to storage layers to reduce data scanned over network and disk.
  • Debug and resolve data skew in distributed joins by salting keys or using adaptive query execution.
  • Monitor and analyze query performance using execution DAGs, stage-level metrics, and wait time breakdowns.

Module 4: Data Consistency and Transaction Management

  • Implement ACID transactions in data lakes using Delta Lake, Apache Iceberg, or Apache Hudi to support upserts and time travel.
  • Configure optimistic concurrency control in transactional table formats to handle write conflicts in multi-writer environments.
  • Design idempotent write operations to ensure exactly-once semantics in streaming data pipelines.
  • Manage snapshot isolation levels to balance consistency and read performance for analytical queries.
  • Implement two-phase commit or distributed locking when coordinating writes across heterogeneous systems (e.g., DB + message queue).
  • Handle schema evolution in transactional tables while preserving backward compatibility for downstream consumers.
  • Reconcile eventual consistency in distributed systems using conflict-free replicated data types (CRDTs) or application-level resolution logic.
  • Monitor and alert on transaction log growth and compaction frequency to prevent performance degradation.

Module 5: Data Governance and Metadata Management

  • Implement centralized metadata catalogs with lineage tracking to map data flow from source to consumption.
  • Enforce data classification and sensitivity tagging using automated scanners and policy engines (e.g., Apache Atlas, AWS DataZone).
  • Integrate data quality rules (e.g., Great Expectations, Deequ) into pipelines to validate completeness, uniqueness, and accuracy.
  • Configure access control policies using attribute-based (ABAC) or role-based (RBAC) models at the column and row level.
  • Automate data retention and purge workflows based on regulatory requirements (e.g., GDPR, CCPA).
  • Standardize data naming, ownership, and documentation practices across teams using metadata annotation tools.
  • Implement data versioning and audit trails to support reproducibility and compliance audits.
  • Integrate data catalog with CI/CD pipelines to validate schema and policy changes before deployment.

Module 6: Security and Compliance in Distributed Environments

  • Implement end-to-end encryption for data at rest (via KMS) and in transit (via TLS) across distributed components.
  • Configure fine-grained access control using Ranger or Sentry policies integrated with LDAP/Active Directory.
  • Enable audit logging for all data access and administrative operations, and centralize logs for SIEM analysis.
  • Mask or tokenize sensitive fields (PII, PCI) dynamically at query time using secure UDFs or proxy layers.
  • Conduct regular security posture assessments, including vulnerability scanning and configuration drift detection.
  • Isolate workloads using network segmentation, VPCs, and private subnets to limit lateral movement.
  • Apply least-privilege principles when granting service account permissions for ETL jobs and APIs.
  • Validate compliance with data sovereignty laws by restricting data storage and processing to approved regions.

Module 7: Performance Monitoring and Observability

  • Instrument pipelines with distributed tracing (e.g., OpenTelemetry) to identify latency bottlenecks across services.
  • Collect and analyze metrics (CPU, memory, I/O, GC) from all cluster nodes using Prometheus or similar tools.
  • Set up alerts for critical thresholds such as disk saturation, job failure rates, or consumer lag.
  • Correlate infrastructure metrics with application logs using centralized logging (e.g., ELK, Splunk).
  • Profile data skew and hotspotting in distributed shuffles using executor-level metrics and stage histograms.
  • Implement synthetic transactions to validate pipeline correctness and measure end-to-end latency.
  • Use replay mechanisms to test pipeline behavior under historical load conditions.
  • Document and maintain baseline performance profiles for capacity planning and anomaly detection.

Module 8: Disaster Recovery and High Availability Planning

  • Design multi-region replication strategies for critical data stores using active-passive or active-active configurations.
  • Test failover procedures for stateful components (e.g., ZooKeeper, NameNode) to ensure minimal downtime.
  • Implement automated backup and restore workflows for metadata stores (e.g., Hive Metastore, Ranger policies).
  • Validate data consistency across replicas using checksums or reconciliation jobs after failover.
  • Configure cross-cluster data synchronization using tools like DistCp or cloud-native replication services.
  • Define RTO and RPO targets for each data tier and align architecture accordingly.
  • Conduct regular disaster recovery drills with full-stack rollback and data validation steps.
  • Document and version control all recovery runbooks and coordinate with incident response teams.

Module 9: Cost Management and Resource Optimization

  • Right-size cluster resources (CPU, memory, disk) based on historical utilization and workload patterns.
  • Implement autoscaling policies for compute fleets using metrics like queue depth or CPU utilization.
  • Use spot instances or preemptible VMs for fault-tolerant batch workloads to reduce compute costs.
  • Optimize storage costs by compressing data, removing duplicates, and transitioning to lower-cost tiers.
  • Monitor and allocate costs by team, project, or department using tagging and cloud billing APIs.
  • Evaluate total cost of ownership (TCO) when choosing between managed and self-hosted services.
  • Identify and decommission orphaned data assets and idle clusters to eliminate waste.
  • Implement query cost estimation and budget enforcement to prevent runaway analytics jobs.
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