Data Storage in Big Data

Description

What does the Data Storage in Big Data course cover?

Data Storage in Big Data is covered here in 9 modules: Architectural Patterns for Scalable Data Storage, Distributed File Systems and Object Storage Integration, NoSQL Database Selection and Deployment and 6 more. The outline lists 72 specific topics, opening with select between lambda and kappa architectures based on real-time processing requirements and data reprocessing frequency.

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

The work is sequenced in 9 stages. It starts with Architectural Patterns for Scalable Data Storage, moves through Distributed File Systems and Object Storage Integration and NoSQL Database Selection and Deployment, and ends at Monitoring, Observability, and Capacity Planning. Each stage carries its own topic list, so the sequence is followed rather than summarised.

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

Module 1 is Architectural Patterns for Scalable Data Storage. It works through select between lambda and kappa architectures based on real-time processing requirements and data reprocessing frequency., implement sharding strategies in distributed databases to balance load and minimize cross-node queries., decide on data partitioning schemes (e.g., range, hash, list) considering query patterns and data growth projections. and 5 more.

How is the Data Storage in Big Data course delivered?

The Data Storage 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 Storage in Big Data course cost?

The Data Storage in Big Data course is $300 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.

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

This curriculum spans the design, deployment, and operational governance of large-scale data storage systems, comparable in scope to a multi-workshop technical advisory program for enterprise data platform teams.

Module 1: Architectural Patterns for Scalable Data Storage

Select between lambda and kappa architectures based on real-time processing requirements and data reprocessing frequency.
Implement sharding strategies in distributed databases to balance load and minimize cross-node queries.
Decide on data partitioning schemes (e.g., range, hash, list) considering query patterns and data growth projections.
Evaluate consistency models (strong, eventual, causal) in distributed storage systems for application-specific tolerance.
Design replication topologies (multi-master vs. leader-follower) based on geographic distribution and failover needs.
Integrate tiered storage layers (hot, warm, cold) using automated lifecycle policies to manage cost and access latency.
Configure distributed file systems (e.g., HDFS, Ceph) with appropriate block sizes and replication factors for workload profiles.
Assess the impact of schema-on-read versus schema-on-write on ingestion pipelines and downstream analytics.

Module 2: Distributed File Systems and Object Storage Integration

Configure HDFS NameNode high availability with shared storage or Quorum Journal Manager to prevent single points of failure.
Optimize data locality in HDFS by aligning compute nodes with storage nodes in cluster topology.
Map S3-compatible object storage semantics (e.g., eventual consistency, PUT idempotency) to application retry logic.
Implement server-side encryption and bucket policies in object storage to enforce data-at-rest protection.
Design multipart upload and resumable transfer mechanisms for large object ingestion over unstable networks.
Integrate object storage with compute frameworks using appropriate connectors (e.g., S3A, ABFS) and credential management.
Manage object versioning and lifecycle transitions in cloud storage to support compliance and cost control.
Address performance bottlenecks in object storage by leveraging caching layers or data placement optimization.

Module 3: NoSQL Database Selection and Deployment

Choose between wide-column, document, and key-value stores based on access patterns and data relationships.
Size cluster nodes and provision IOPS for Cassandra based on data volume, compaction strategy, and read/write ratios.
Design MongoDB sharded clusters with appropriate shard keys to avoid hotspots and ensure even distribution.
Configure DynamoDB provisioned capacity or enable on-demand mode based on traffic predictability and cost constraints.
Implement time-to-live (TTL) policies in NoSQL databases for automatic expiration of transient data.
Manage secondary index usage in NoSQL systems to balance query flexibility with write performance degradation.
Enforce row-level security in document databases using query filters or application-level enforcement.
Plan backup and restore procedures for distributed NoSQL clusters, including snapshot consistency and cross-region replication.

Module 4: Data Lake Design and Metadata Management

Define a consistent naming and directory structure convention for data lake zones (raw, curated, trusted).
Implement metadata extraction pipelines to populate a central catalog during data ingestion.
Select file formats (Parquet, ORC, Avro) based on compression, schema evolution, and query engine compatibility.
Configure ACID transaction support in data lakes using Delta Lake, Apache Iceberg, or Hudi.
Design partitioning and clustering strategies in data lake tables to optimize query performance.
Manage schema drift detection and enforcement in evolving data sources ingested into the lake.
Integrate data lake with a centralized metadata repository for lineage and impact analysis.
Implement soft deletes and time-travel capabilities using snapshot isolation in transactional table formats.

Module 5: Data Governance and Security in Distributed Storage

Implement column- and row-level access controls in storage systems using Ranger, Sentry, or native policies.
Integrate storage layers with enterprise identity providers using LDAP or SAML-based authentication.
Apply data masking or dynamic redaction in query engines for sensitive fields based on user roles.
Configure audit logging for data access and administrative operations across storage components.
Enforce encryption for data in transit using TLS and for data at rest using KMS-integrated key management.
Classify data sensitivity levels and apply retention and disposal policies accordingly.
Implement data retention and archival rules aligned with regulatory requirements (e.g., GDPR, HIPAA).
Conduct periodic access reviews and permission cleanup to prevent privilege creep.

Module 6: Performance Optimization and Cost Management

Monitor and tune garbage collection and compaction in distributed databases to minimize I/O spikes.
Optimize query performance by aligning storage layout with common filter and join patterns.
Use data compaction and file merging in data lakes to reduce small file overhead.
Implement caching strategies (e.g., Alluxio, Redis) for frequently accessed datasets.
Right-size cluster resources based on utilization metrics and workload patterns.
Compare storage cost per terabyte across cloud providers and storage classes for archival workloads.
Apply compression algorithms (Snappy, Zstandard, Gzip) based on CPU overhead and storage savings trade-offs.
Use query cost estimation tools to evaluate the impact of storage design on compute expenses.

Module 7: Data Ingestion and Pipeline Resilience

Design idempotent ingestion pipelines to handle duplicate messages from message queues or logs.
Implement backpressure mechanisms in streaming ingestion to prevent overload during consumer lag.
Select between batch and micro-batch ingestion based on latency requirements and source system capabilities.
Configure Kafka topics with appropriate retention, replication, and partitioning for downstream consumers.
Handle schema validation and rejection of malformed records during ingestion using schema registries.
Monitor end-to-end data latency and implement alerting for pipeline degradation.
Use checkpointing and offset management to ensure exactly-once or at-least-once processing semantics.
Implement retry logic with exponential backoff for transient failures in data transfer operations.

Module 8: Disaster Recovery and High Availability Planning

Define RPO and RTO for critical data stores and align replication and backup strategies accordingly.
Configure cross-region replication for object storage and distributed databases to support failover.
Test failover procedures for primary storage systems under simulated network partition scenarios.
Store backup snapshots in immutable or write-once-read-many (WORM) storage to prevent ransomware deletion.
Validate data consistency across replicas using checksums or reconciliation jobs.
Document recovery runbooks with step-by-step procedures for different outage scenarios.
Implement automated monitoring for replication lag and trigger alerts when thresholds are exceeded.
Conduct periodic disaster recovery drills to evaluate recovery time and data integrity.

Module 9: Monitoring, Observability, and Capacity Planning

Instrument storage systems with metrics collection for latency, throughput, and error rates.
Set up alerts for disk utilization thresholds to prevent out-of-space conditions.
Correlate storage performance metrics with application-level SLAs to identify bottlenecks.
Use distributed tracing to track data access paths across storage and compute layers.
Forecast storage capacity needs based on historical growth trends and business projections.
Implement tagging and labeling strategies to track cost allocation by department or project.
Generate usage reports to identify underutilized or orphaned data for cleanup.
Integrate storage monitoring into centralized observability platforms (e.g., Prometheus, Grafana).