Data Engineering patterns on the cloud
Data Engineering patterns on the cloud
How to solve common data engineering problems with cloud services?
About the Book
Doing data engineering without the cloud is hard to imagine nowadays. Does it mean, each cloud provider is different? Well, yes, each of them has its own specificities but it doesn't mean they're completely different. They all support several common data engineering patterns that you will find in the book classified in 7 categories: data processing, data storage, data security, data warehouse, data management, data orchestration and data transfer. This organization and the explanation of each pattern will help you understand a new cloud provider easier, whenever you come from AWS, Azure, or GCP environment.
- Who is this book for?
- Book organization
- Conventions used in the book
- Data processing
- Auto-terminated clusters
- Consumer groups
- Cooldown period
- Data locality
- Dead-Letter sink
- Ephemeral cluster
- Event-driven serverless worker
- Exactly-once delivery
- Flexible jobs
- Micro-batch processing
- No Code data processing
- NoSQL Change Data Capture
- Ordered delivery
- Parallel data upload
- Pull processing
- Push processing
- Reprocessing data from a streaming data source
- Reusable job template
- Serverless SQL worker
- Shuffle service
- Single-tenant client
- Small data - batch services
- Small data - single-node cluster
- Streaming serverless SQL processing
- Targeted data retrieval from files
- Data storage
- Auto-scaled throughput capacity
- Data denormalization with nested and repeated structures
- Data layout - partitions
- Data layout - secondary partitions
- Optimized data access - indexes
- Optimized data access - row keys
- Immutable storage
- Interleaved storage
- Multi-region storage
- Serverless database
- Streaming data retention
- Streaming-to-object store synchronization
- Throughput capacity reservation
- Data security
- Access without cloud identity
- Cross-account data access with service accounts
- Custom permissions
- Data encryption at rest
- Data encryption in transit
- Data rollback
- Denied public access
- Identity-based access
- Network access control
- Network separation
- Policy conditions
- Private communication with cloud services from a cloud network
- Resource-based permissions
- Safe secrets storage
- Shared permissions
- Data warehouse
- Fault-tolerance - backups
- Batched DML operations
- Big dataset loading
- Column-level security
- Extract Load
- Data distribution - key-based
- Data distribution - replicated
- Data distribution - round-robin
- Identity management service access
- Materialized views
- Nested data storage
- Result set cache
- Row-level security
- Temporary table
- Querying - external data sources
- Querying - User Defined Functions
- Audit logs
- Automatic data classification
- Automatic data expiration
- Automatic lifecycle management
- Data annotation
- Data cataloging - automatic
- Data cataloging - manual
- Intelligent lifecycle management
- Schema registry
- Data processing service orchestrator
- No Code orchestrator
- Open Source orchestrator as a service
- Serverless orchestrator
- Time-based trigger
- Data transfer
- Continuous data migration
- Heterogeneous database migration
- Homogeneous database migration
- One-time data migration
- Transfer without data movement
- About the author
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