Osoby które kupowały "Designing Data-Intensive Applications. The Big Ideas Behind Reliable, Scalable, and Maintainable Systems", wybierały także:

• Data Science w Pythonie. Kurs video. Algorytmy uczenia maszynowego 199,00 zÅ‚, (59,70 zÅ‚ -70%)
• Power BI Desktop. Kurs video. Wykorzystanie narzÄ™dzia w analizie i wizualizacji danych 349,00 zÅ‚, (104,70 zÅ‚ -70%)
• Statystyka. Kurs video. Przewodnik dla student 128,71 zÅ‚, (39,90 zÅ‚ -69%)
• Microsoft Excel. Kurs video. Wykresy i wizualizacja danych 199,00 zÅ‚, (69,65 zÅ‚ -65%)
• Analiza danych w Tableau. Kurs video. Podstawy pracy analityka 249,00 zÅ‚, (87,15 zÅ‚ -65%)

Spis treści

Designing Data-Intensive Applications. The Big Ideas Behind Reliable, Scalable, and Maintainable Systems eBook -- spis treści

• Preface
  • Who Should Read This Book?
  • Scope of This Book
  • Outline of This Book
  • References and Further Reading
  • OReilly Safari
  • How to Contact Us
  • Acknowledgments
• I. Foundations of Data Systems
• 1. Reliable, Scalable, and Maintainable Applications
  • Thinking About Data Systems
  • Reliability
    • Hardware Faults
    • Software Errors
    • Human Errors
    • How Important Is Reliability?
  • Scalability
    • Describing Load
    • Describing Performance
    • Approaches for Coping with Load
  • Maintainability
    • Operability: Making Life Easy for Operations
    • Simplicity: Managing Complexity
    • Evolvability: Making Change Easy
  • Summary
• 2. Data Models and Query Languages
  • Relational Model Versus Document Model
    • The Birth of NoSQL
    • The Object-Relational Mismatch
    • Many-to-One and Many-to-Many Relationships
    • Are Document Databases Repeating History?
      • The network model
      • The relational model
      • Comparison to document databases
    • Relational Versus Document Databases Today
      • Which data model leads to simpler application code?
      • Schema flexibility in the document model
      • Data locality for queries
      • Convergence of document and relational databases
  • Query Languages for Data
    • Declarative Queries on the Web
    • MapReduce Querying
  • Graph-Like Data Models
    • Property Graphs
    • The Cypher Query Language
    • Graph Queries in SQL
    • Triple-Stores and SPARQL
      • The semantic web
      • The RDF data model
      • The SPARQL query language
    • The Foundation: Datalog
  • Summary
• 3. Storage and Retrieval
  • Data Structures That Power Your Database
    • Hash Indexes
    • SSTables and LSM-Trees
      • Constructing and maintaining SSTables
      • Making an LSM-tree out of SSTables
      • Performance optimizations
    • B-Trees
      • Making B-trees reliable
      • B-tree optimizations
    • Comparing B-Trees and LSM-Trees
      • Advantages of LSM-trees
      • Downsides of LSM-trees
    • Other Indexing Structures
      • Storing values within the index
      • Multi-column indexes
      • Full-text search and fuzzy indexes
      • Keeping everything in memory
  • Transaction Processing or Analytics?
    • Data Warehousing
      • The divergence between OLTP databases and data warehouses
    • Stars and Snowflakes: Schemas for Analytics
  • Column-Oriented Storage
    • Column Compression
      • Memory bandwidth and vectorized processing
    • Sort Order in Column Storage
      • Several different sort orders
    • Writing to Column-Oriented Storage
    • Aggregation: Data Cubes and Materialized Views
  • Summary
• 4. Encoding and Evolution
  • Formats for Encoding Data
    • Language-Specific Formats
    • JSON, XML, and Binary Variants
      • Binary encoding
    • Thrift and Protocol Buffers
      • Field tags and schema evolution
      • Datatypes and schema evolution
    • Avro
      • The writers schema and the readers schema
      • Schema evolution rules
      • But what is the writers schema?
      • Dynamically generated schemas
      • Code generation and dynamically typed languages
    • The Merits of Schemas
  • Modes of Dataflow
    • Dataflow Through Databases
      • Different values written at different times
      • Archival storage
    • Dataflow Through Services: REST and RPC
      • Web services
      • The problems with remote procedure calls (RPCs)
      • Current directions for RPC
      • Data encoding and evolution for RPC
    • Message-Passing Dataflow
      • Message brokers
      • Distributed actor frameworks
  • Summary
• II. Distributed Data
• 5. Replication
  • Leaders and Followers
    • Synchronous Versus Asynchronous Replication
    • Setting Up New Followers
    • Handling Node Outages
      • Follower failure: Catch-up recovery
      • Leader failure: Failover
    • Implementation of Replication Logs
      • Statement-based replication
      • Write-ahead log (WAL) shipping
      • Logical (row-based) log replication
      • Trigger-based replication
  • Problems with Replication Lag
    • Reading Your Own Writes
    • Monotonic Reads
    • Consistent Prefix Reads
    • Solutions for Replication Lag
  • Multi-Leader Replication
    • Use Cases for Multi-Leader Replication
      • Multi-datacenter operation
      • Clients with offline operation
      • Collaborative editing
    • Handling Write Conflicts
      • Synchronous versus asynchronous conflict detection
      • Conflict avoidance
      • Converging toward a consistent state
      • Custom conflict resolution logic
      • What is a conflict?
    • Multi-Leader Replication Topologies
  • Leaderless Replication
    • Writing to the Database When a Node Is Down
      • Read repair and anti-entropy
      • Quorums for reading and writing
    • Limitations of Quorum Consistency
      • Monitoring staleness
    • Sloppy Quorums and Hinted Handoff
      • Multi-datacenter operation
    • Detecting Concurrent Writes
      • Last write wins (discarding concurrent writes)
      • The happens-before relationship and concurrency
      • Capturing the happens-before relationship
      • Merging concurrently written values
      • Version vectors
  • Summary
• 6. Partitioning
  • Partitioning and Replication
  • Partitioning of Key-Value Data
    • Partitioning by Key Range
    • Partitioning by Hash of Key
    • Skewed Workloads and Relieving Hot Spots
  • Partitioning and Secondary Indexes
    • Partitioning Secondary Indexes by Document
    • Partitioning Secondary Indexes by Term
  • Rebalancing Partitions
    • Strategies for Rebalancing
      • How not to do it: hash mod N
      • Fixed number of partitions
      • Dynamic partitioning
      • Partitioning proportionally to nodes
    • Operations: Automatic or Manual Rebalancing
  • Request Routing
    • Parallel Query Execution
  • Summary
• 7. Transactions
  • The Slippery Concept of a Transaction
    • The Meaning of ACID
      • Atomicity
      • Consistency
      • Isolation
      • Durability
    • Single-Object and Multi-Object Operations
      • Single-object writes
      • The need for multi-object transactions
      • Handling errors and aborts
  • Weak Isolation Levels
    • Read Committed
      • No dirty reads
      • No dirty writes
      • Implementing read committed
    • Snapshot Isolation and Repeatable Read
      • Implementing snapshot isolation
      • Visibility rules for observing a consistent snapshot
      • Indexes and snapshot isolation
      • Repeatable read and naming confusion
    • Preventing Lost Updates
      • Atomic write operations
      • Explicit locking
      • Automatically detecting lost updates
      • Compare-and-set
      • Conflict resolution and replication
    • Write Skew and Phantoms
      • Characterizing write skew
      • More examples of write skew
      • Phantoms causing write skew
      • Materializing conflicts
  • Serializability
    • Actual Serial Execution
      • Encapsulating transactions in stored procedures
      • Pros and cons of stored procedures
      • Partitioning
      • Summary of serial execution
    • Two-Phase Locking (2PL)
      • Implementation of two-phase locking
      • Performance of two-phase locking
      • Predicate locks
      • Index-range locks
    • Serializable Snapshot Isolation (SSI)
      • Pessimistic versus optimistic concurrency control
      • Decisions based on an outdated premise
      • Detecting stale MVCC reads
      • Detecting writes that affect prior reads
      • Performance of serializable snapshot isolation
  • Summary
• 8. The Trouble with Distributed Systems
  • Faults and Partial Failures
    • Cloud Computing and Supercomputing
  • Unreliable Networks
    • Network Faults in Practice
    • Detecting Faults
    • Timeouts and Unbounded Delays
      • Network congestion and queueing
    • Synchronous Versus Asynchronous Networks
      • Can we not simply make network delays predictable?
  • Unreliable Clocks
    • Monotonic Versus Time-of-Day Clocks
      • Time-of-day clocks
      • Monotonic clocks
    • Clock Synchronization and Accuracy
    • Relying on Synchronized Clocks
      • Timestamps for ordering events
      • Clock readings have a confidence interval
      • Synchronized clocks for global snapshots
    • Process Pauses
      • Response time guarantees
      • Limiting the impact of garbage collection
  • Knowledge, Truth, and Lies
    • The Truth Is Defined by the Majority
      • The leader and the lock
      • Fencing tokens
    • Byzantine Faults
      • Weak forms of lying
    • System Model and Reality
      • Correctness of an algorithm
      • Safety and liveness
      • Mapping system models to the real world
  • Summary
• 9. Consistency and Consensus
  • Consistency Guarantees
  • Linearizability
    • What Makes a System Linearizable?
    • Relying on Linearizability
      • Locking and leader election
      • Constraints and uniqueness guarantees
      • Cross-channel timing dependencies
    • Implementing Linearizable Systems
      • Linearizability and quorums
    • The Cost of Linearizability
      • The CAP theorem
      • Linearizability and network delays
  • Ordering Guarantees
    • Ordering and Causality
      • The causal order is not a total order
      • Linearizability is stronger than causal consistency
      • Capturing causal dependencies
    • Sequence Number Ordering
      • Noncausal sequence number generators
      • Lamport timestamps
      • Timestamp ordering is not sufficient
    • Total Order Broadcast
      • Using total order broadcast
      • Implementing linearizable storage using total order broadcast
      • Implementing total order broadcast using linearizable storage
  • Distributed Transactions and Consensus
    • Atomic Commit and Two-Phase Commit (2PC)
      • From single-node to distributed atomic commit
      • Introduction to two-phase commit
      • A system of promises
      • Coordinator failure
      • Three-phase commit
    • Distributed Transactions in Practice
      • Exactly-once message processing
      • XA transactions
      • Holding locks while in doubt
      • Recovering from coordinator failure
      • Limitations of distributed transactions
    • Fault-Tolerant Consensus
      • Consensus algorithms and total order broadcast
      • Single-leader replication and consensus
      • Epoch numbering and quorums
      • Limitations of consensus
    • Membership and Coordination Services
      • Allocating work to nodes
      • Service discovery
      • Membership services
  • Summary
• III. Derived Data
• 10. Batch Processing
  • Batch Processing with Unix Tools
    • Simple Log Analysis
      • Chain of commands versus custom program
      • Sorting versus in-memory aggregation
    • The Unix Philosophy
      • A uniform interface
      • Separation of logic and wiring
      • Transparency and experimentation
  • MapReduce and Distributed Filesystems
    • MapReduce Job Execution
      • Distributed execution of MapReduce
      • MapReduce workflows
    • Reduce-Side Joins and Grouping
      • Example: analysis of user activity events
      • Sort-merge joins
      • Bringing related data together in the same place
      • GROUP BY
      • Handling skew
    • Map-Side Joins
      • Broadcast hash joins
      • Partitioned hash joins
      • Map-side merge joins
      • MapReduce workflows with map-side joins
    • The Output of Batch Workflows
      • Building search indexes
      • Key-value stores as batch process output
      • Philosophy of batch process outputs
    • Comparing Hadoop to Distributed Databases
      • Diversity of storage
      • Diversity of processing models
      • Designing for frequent faults
  • Beyond MapReduce
    • Materialization of Intermediate State
      • Dataflow engines
      • Fault tolerance
      • Discussion of materialization
    • Graphs and Iterative Processing
      • The Pregel processing model
      • Fault tolerance
      • Parallel execution
    • High-Level APIs and Languages
      • The move toward declarative query languages
      • Specialization for different domains
  • Summary
• 11. Stream Processing
  • Transmitting Event Streams
    • Messaging Systems
      • Direct messaging from producers to consumers
      • Message brokers
      • Message brokers compared to databases
      • Multiple consumers
      • Acknowledgments and redelivery
    • Partitioned Logs
      • Using logs for message storage
      • Logs compared to traditional messaging
      • Consumer offsets
      • Disk space usage
      • When consumers cannot keep up with producers
      • Replaying old messages
  • Databases and Streams
    • Keeping Systems in Sync
    • Change Data Capture
      • Implementing change data capture
      • Initial snapshot
      • Log compaction
      • API support for change streams
    • Event Sourcing
      • Deriving current state from the event log
      • Commands and events
    • State, Streams, and Immutability
      • Advantages of immutable events
      • Deriving several views from the same event log
      • Concurrency control
      • Limitations of immutability
  • Processing Streams
    • Uses of Stream Processing
      • Complex event processing
      • Stream analytics
      • Maintaining materialized views
      • Search on streams
      • Message passing and RPC
    • Reasoning About Time
      • Event time versus processing time
      • Knowing when youre ready
      • Whose clock are you using, anyway?
      • Types of windows
    • Stream Joins
      • Stream-stream join (window join)
      • Stream-table join (stream enrichment)
      • Table-table join (materialized view maintenance)
      • Time-dependence of joins
    • Fault Tolerance
      • Microbatching and checkpointing
      • Atomic commit revisited
      • Idempotence
      • Rebuilding state after a failure
  • Summary
• 12. The Future of Data Systems
  • Data Integration
    • Combining Specialized Tools by Deriving Data
      • Reasoning about dataflows
      • Derived data versus distributed transactions
      • The limits of total ordering
      • Ordering events to capture causality
    • Batch and Stream Processing
      • Maintaining derived state
      • Reprocessing data for application evolution
      • The lambda architecture
      • Unifying batch and stream processing
  • Unbundling Databases
    • Composing Data Storage Technologies
      • Creating an index
      • The meta-database of everything
      • Making unbundling work
      • Unbundled versus integrated systems
      • Whats missing?
    • Designing Applications Around Dataflow
      • Application code as a derivation function
      • Separation of application code and state
      • Dataflow: Interplay between state changes and application code
      • Stream processors and services
    • Observing Derived State
      • Materialized views and caching
      • Stateful, offline-capable clients
      • Pushing state changes to clients
      • End-to-end event streams
      • Reads are events too
      • Multi-partition data processing
  • Aiming for Correctness
    • The End-to-End Argument for Databases
      • Exactly-once execution of an operation
      • Duplicate suppression
      • Operation identifiers
      • The end-to-end argument
      • Applying end-to-end thinking in data systems
    • Enforcing Constraints
      • Uniqueness constraints require consensus
      • Uniqueness in log-based messaging
      • Multi-partition request processing
    • Timeliness and Integrity
      • Correctness of dataflow systems
      • Loosely interpreted constraints
      • Coordination-avoiding data systems
    • Trust, but Verify
      • Maintaining integrity in the face of software bugs
      • Dont just blindly trust what they promise
      • A culture of verification
      • Designing for auditability
      • The end-to-end argument again
      • Tools for auditable data systems
  • Doing the Right Thing
    • Predictive Analytics
      • Bias and discrimination
      • Responsibility and accountability
      • Feedback loops
    • Privacy and Tracking
      • Surveillance
      • Consent and freedom of choice
      • Privacy and use of data
      • Data as assets and power
      • Remembering the Industrial Revolution
      • Legislation and self-regulation
  • Summary
• Glossary
• Index