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Database Administration in Big Data

Database Administration in Big Data

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This curriculum spans the technical breadth of a multi-phase database modernization initiative, covering the design, deployment, and operational governance of distributed data systems at the scale and complexity typical of enterprise data platform migrations.

Module 1: Architecting Scalable Data Storage for Big Data Environments

  • Selecting between distributed file systems (e.g., HDFS) and object storage (e.g., S3) based on access patterns, durability requirements, and integration with processing frameworks.
  • Designing data partitioning strategies in HDFS to balance node utilization and minimize data skew across clusters.
  • Configuring replication factors in HDFS to meet fault tolerance SLAs while managing storage overhead.
  • Implementing tiered storage policies to migrate cold data from high-performance disks to cost-effective archival storage.
  • Evaluating erasure coding versus replication for large-scale data sets to optimize storage efficiency and recovery performance.
  • Integrating cloud-native storage services with on-prem Hadoop clusters using gateway solutions, considering latency and data consistency.
  • Planning namespace federation in large HDFS clusters to overcome NameNode scalability limitations.
  • Enforcing encryption at rest for data blocks using HDFS Transparent Data Encryption (TDE) with centralized key management.

Module 2: Deploying and Managing Distributed Database Clusters

  • Choosing between Apache Cassandra, HBase, and ScyllaDB based on consistency model, write throughput, and operational complexity.
  • Configuring replication strategies (e.g., NetworkTopologyStrategy in Cassandra) to align with data center topology and disaster recovery requirements.
  • Setting compaction strategies (e.g., Size-Tiered vs. Time-Window) in Cassandra based on data ingestion rate and query access patterns.
  • Managing region splits and merges in HBase to prevent hotspotting and maintain balanced cluster performance.
  • Implementing automated node repair processes using tools like Cassandra Reaper with scheduling and failure handling.
  • Configuring quorum-based read/write consistency levels to balance availability and data correctness during node failures.
  • Planning cluster expansion by adding nodes incrementally and rebalancing data without service interruption.
  • Securing inter-node communication using TLS and mutual authentication in distributed databases.

Module 3: Data Ingestion Pipeline Design and Optimization

  • Selecting ingestion tools (e.g., Apache Kafka, Flume, Nifi) based on data velocity, schema requirements, and fault tolerance needs.
  • Designing Kafka topic partitioning schemes to support parallel consumers and maintain message ordering within logical groups.
  • Configuring Kafka retention policies and log compaction for event sourcing and state recovery use cases.
  • Implementing schema validation and evolution using Schema Registry with Avro for backward and forward compatibility.
  • Handling backpressure in streaming pipelines by tuning consumer group offsets and buffer sizes.
  • Deploying change data capture (CDC) from RDBMS sources using Debezium, managing transaction log polling and latency.
  • Monitoring end-to-end data latency across ingestion stages using distributed tracing and timestamp watermarking.
  • Securing data in transit between ingestion components using TLS and SASL authentication.

Module 4: Performance Tuning and Query Optimization

  • Configuring JVM heap size and garbage collection settings for long-running database processes to reduce pause times.
  • Indexing strategies in columnar databases (e.g., secondary indexes in HBase, materialized views in Cassandra).
  • Optimizing Hive queries by enabling vectorization, cost-based optimization, and predicate pushdown.
  • Tuning Spark executors (memory, cores, instances) to maximize resource utilization in YARN-managed clusters.
  • Partitioning and bucketing large Hive tables to reduce scan overhead and improve join performance.
  • Using caching mechanisms (e.g., Alluxio, Spark caching) for frequently accessed datasets while managing memory pressure.
  • Diagnosing slow queries using execution plans and identifying bottlenecks such as data shuffles or skew.
  • Implementing query queuing and concurrency limits in HiveServer2 to prevent resource exhaustion.

Module 5: Security, Access Control, and Compliance

  • Implementing role-based access control (RBAC) in Apache Ranger or Sentry for fine-grained access to databases and tables.
  • Enforcing column- and row-level security policies to restrict sensitive data access based on user roles.
  • Integrating Kerberos authentication across Hadoop components and managing keytab lifecycle.
  • Configuring audit logging in Ranger or HDFS to capture access events and support compliance reporting.
  • Masking sensitive data fields dynamically using Ranger policies during query execution.
  • Managing encryption zones in HDFS and ensuring proper delegation token propagation for encrypted directories.
  • Aligning data retention and deletion workflows with GDPR, CCPA, or industry-specific compliance mandates.
  • Conducting regular access certification reviews to identify and remediate excessive privileges.

Module 6: High Availability and Disaster Recovery Planning

  • Configuring HDFS HA with standby NameNodes and automatic failover using ZooKeeper.
  • Implementing HBase replication across clusters for active-passive or active-active configurations.
  • Designing cross-region Kafka mirroring using MirrorMaker 2.0 with offset translation and topic mapping.
  • Scheduling and validating full and incremental backups for distributed databases using native tools or custom scripts.
  • Testing failover procedures for critical services (e.g., Hive Metastore, YARN ResourceManager) in staging environments.
  • Establishing recovery point (RPO) and recovery time (RTO) objectives for different data tiers.
  • Documenting and automating runbooks for common failure scenarios such as NameNode crash or ZooKeeper quorum loss.
  • Validating backup integrity by restoring to isolated environments and verifying data consistency.

Module 7: Monitoring, Alerting, and Capacity Management

  • Deploying monitoring agents (e.g., Prometheus Node Exporter, Cloudera Manager agents) across cluster nodes.
  • Defining key performance indicators (KPIs) such as disk utilization, GC time, and RPC queue depth for early detection of issues.
  • Configuring alert thresholds in Grafana or Nagios to minimize false positives while capturing critical failures.
  • Correlating logs from multiple components (e.g., HDFS, YARN, Kafka) using centralized logging with Elasticsearch and Kibana.
  • Tracking long-term capacity trends to forecast storage and compute needs based on growth rates.
  • Identifying underutilized nodes or idle services for rightsizing or decommissioning.
  • Using JMX metrics to monitor internal database states such as memtable size, pending compactions, or replication lag.
  • Integrating monitoring data with ITSM tools for incident ticketing and escalation workflows.

Module 8: Governance, Metadata Management, and Data Lineage

  • Implementing a centralized metastore using Hive Metastore or AWS Glue for cross-engine schema consistency.
  • Automating metadata extraction from ETL jobs and registering datasets in Apache Atlas.
  • Configuring classification and tagging frameworks in Atlas to support data cataloging and policy enforcement.
  • Tracking end-to-end data lineage from source ingestion to reporting layers using lineage capture tools.
  • Enforcing data quality rules at ingestion and transformation stages using Great Expectations or Deequ.
  • Managing schema change approvals through version-controlled DDL scripts and migration tools.
  • Resolving metadata inconsistencies caused by direct HDFS file manipulation or out-of-band schema updates.
  • Integrating data governance policies with CI/CD pipelines for automated validation of data models.

Module 9: Cloud Migration and Hybrid Architecture Strategies

  • Evaluating lift-and-shift versus refactored migration approaches for on-prem Hadoop clusters to cloud platforms.
  • Designing hybrid data architectures where on-prem systems coexist with cloud data lakes (e.g., S3, ADLS).
  • Migrating large datasets using high-bandwidth transfer appliances or accelerated network services.
  • Re-architecting workloads to leverage managed services (e.g., Amazon EMR, Azure HDInsight) while retaining control.
  • Managing identity federation between on-prem Kerberos and cloud IAM using identity brokers.
  • Optimizing cross-cloud data transfer costs by compressing data and scheduling off-peak transfers.
  • Implementing consistent backup and DR policies across hybrid environments using cloud-native tools.
  • Monitoring performance and cost implications of egress traffic between cloud regions and on-prem networks.
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