Categories

8 categories covering 69 posts.

Career1 Hudi11 Iceberg24 Interview4 Lakehouse6 Linux1 Spark20 Tools2

Career 1

  1. 50 job websites for remote and tech roles, and how to use them well
    #Remote #Jobs #Search #ATS

Hudi 11

  1. One Hudi write, two table formats: committing Iceberg metadata inside the Hudi commit
    #Hudi #Iceberg #XTable #Interoperability
  2. Apache Hudi: an introduction to its architecture, features and what makes it different
    #Hudi #Lakehouse #Spark #Indexing #Streaming
  3. Apache Hudi architecture: what actually happens when you upsert a row
    #Hudi #Architecture #Internals #Compaction #Indexing
  4. The Hudi index: how one lookup decides the cost of every upsert
    #Hudi #Indexing #Metadata #Upserts
  5. Hudi Merge-on-Read: the write you skipped, and the read that pays for it
    #Hudi #Compaction #Timeline #Concurrency
  6. Apache Hudi on Spark: the complete cheat sheet
    #Hudi #Lakehouse #Spark #PySpark #Reference
  7. Apache Hudi through Spark Connect: running the full table surface from a thin client
    #Spark #SparkConnect #Hudi #Lakehouse
  8. Hudi key generators: the one decision you cannot change later
    #Hudi #KeyGenerator #Partitioning #Configuration
  9. Concurrency control in Hudi: the lock provider that protects nothing
    #Hudi #Concurrency #Locking #Timeline #Production
  10. Incremental processing in Hudi: reading only what changed, and the timestamp that betrays you
    #Hudi #Incremental #CDC #Timeline #Streaming
  11. Spark and Hudi logging: making a write explain itself
    #Hudi #Spark #Logging #Log4j #Troubleshoot

Iceberg 24

  1. Apache Iceberg 1.12.0: what's new, breaking changes, and upgrade guide
    #Iceberg #Lakehouse #Spark #Flink #KafkaConnect #Release #Upgrade
  2. What Hive tables could not do, and why Iceberg exists
    #Iceberg #Hive #Metadata #Lakehouse
  3. A local Iceberg playground: Spark, MinIO and a REST catalog in one compose file
    #Iceberg #MinIO #Docker #Catalog
  4. The three tiers of an Iceberg table: catalog, metadata, data
    #Iceberg #Metadata #Manifests #Architecture
  5. Apache Iceberg on Spark: the complete cheat sheet
    #Iceberg #Lakehouse #Spark #PySpark #Reference
  6. Apache Iceberg architecture: what actually happens when you commit
    #Iceberg #Architecture #Internals #Snapshots #Catalog
  7. Iceberg catalogs: the one pointer that makes a table a table
    #Iceberg #Catalog #REST #Governance
  8. Life of a write query in Iceberg: three files, one pointer swap, and what ACID means here
    #Iceberg #Commits #Concurrency #Transactions
  9. Life of a read query in Iceberg: four prunes before a byte of data is read
    #Iceberg #Pruning #Metadata #Query
  10. Iceberg format versions: what v1, v2, v3 and v4 actually change
    #Iceberg #Deletes #Lineage #Compatibility
  11. Schema evolution in Iceberg: why a rename costs nothing
    #Iceberg #Schema #Evolution #Compatibility
  12. Hidden partitioning and partition evolution: changing layout without rewriting history
    #Iceberg #Partitioning #Pruning #Transforms
  13. Loading data into Iceberg: appends, overwrites and row-level SQL
    #Iceberg #Ingestion #Merge #Overwrite
  14. Streaming into Iceberg: availableNow, checkpoints, and the snapshot count nobody watches
    #Iceberg #Streaming #Checkpoints #Snapshots
  15. Copy-on-write or merge-on-read: choosing how an Iceberg table pays for a delete
    #Iceberg #Deletes #Compaction #Streaming
  16. CDC into Iceberg: the delete files nobody counted
    #Iceberg #CDC #Deletes #Compaction
  17. Time travel and rollback in Iceberg: undoing a bad write in one statement
    #Iceberg #Snapshots #Recovery #History
  18. Apache Iceberg through Spark Connect: the full surface, and the extension that breaks time travel
    #Spark #SparkConnect #Iceberg #Lakehouse
  19. Running Iceberg on Apache Polaris: one compose file, two storage backends
    #Iceberg #Polaris #Catalog #Spark #MinIO
  20. Branching, tagging and write-audit-publish in Iceberg: CI for a table
    #Iceberg #Branching #Tagging #WAP #Production
  21. Iceberg views: a versioned view definition, and the cross-engine promise tested
    #Iceberg #Views #Catalogs #Trino #Interoperability
  22. Migrating Hive tables to Iceberg: snapshot first, migrate second
    #Iceberg #Migration #Hive #Parquet
  23. Sorting and clustering in Iceberg: what min/max statistics can and cannot skip
    #Iceberg #Performance #Sorting #Clustering #Maintenance
  24. Running Iceberg in production: file sizes, object-storage paths, and the jobs you must schedule
    #Iceberg #Production #Tuning #Storage

Interview 4

  1. System Design for Data Engineers: A Framework, the Building Blocks, and Worked Designs
    #Pipelines #Streaming #Lakehouse #Kafka
  2. Top Data engineer interview questions
    #SQL #Spark #Streaming #Design
  3. Data Modeling for Data Engineers: From Foundations to Lakehouse Design
    #Modeling #Warehouse #Lakehouse #Spark
  4. Top Apache Spark interview questions, with answers
    #Spark #Interview #Reference #Scenarios

Lakehouse 6

  1. Open table formats in practice: what actually differs between Iceberg, Hudi and Delta Lake
    #Iceberg #Hudi #Delta #Lakehouse #Spark
  2. Run every demo on this blog locally: one compose stack for Hudi, Iceberg and Delta
    #Docker #Iceberg #Hudi #Delta
  3. Apache XTable incremental sync: how to keep your conversions fast
    #XTable #Hudi #Iceberg #Delta #Lakehouse
  4. Apache XTable cheat sheet: one table, every format
    #XTable #Hudi #Iceberg #Delta #Paimon #Reference
  5. Table maintenance for Iceberg and Hudi: compaction, expiry, and the space you did not reclaim
    #Iceberg #Hudi #Compaction #Cleaning
  6. One table, three formats: Iceberg, Delta and Hudi interoperability
    #Iceberg #Delta #Hudi #XTable

Linux 1

  1. Kerberos Setup in Linux
    #Linux #Kerberos #Security

Spark 20

  1. Spark 4 in four releases: the features worth knowing and what they cost
    #Spark #SQL #Streaming #PySpark
  2. Spark on Kubernetes on your Mac: kind, spark-submit, the Spark Operator and Iceberg
    #Spark #Kubernetes #Operator #Iceberg
  3. Apache Spark architecture: what actually happens when you call an action
    #Spark #Architecture #Internals #Shuffle #Scheduling
  4. Apache Spark: the complete cheat sheet
    #Spark #SQL #Tuning #Streaming #Reference
  5. Spark Submit Command Formatter tool
    #Spark #Utilities
  6. Spark Configuration Generator tool
    #Spark #Utilities #Generator
  7. Inside Spark's Catalyst optimizer: how a query gets rewritten, rule by rule
    #Spark #Catalyst #Optimizer #Internals #Codegen
  8. Adaptive query execution in Apache Spark: what it re-plans, and what it leaves alone
    #Spark #AQE #Performance #Tuning #Internals
  9. Apache Spark joins in depth: the five strategies and how one gets picked
    #Spark #Joins #Catalyst #Performance #Internals
  10. Spark memory management: every region, and the arithmetic that sizes it
    #Spark #Memory #Tuning #Internals #Performance
  11. Spark shuffle internals: the three writers, and which one your job gets
    #Spark #Shuffle #Serialization #Internals
  12. Data skew in Apache Spark: how to see it, and nine ways to fix it
    #Spark #Skew #Performance #Joins #Tuning
  13. Spark performance tuning: seventeen techniques, and how to prove each one worked
    #Spark #Performance #Joins #AQE
  14. Native Spark execution: Apache Gluten and DataFusion Comet compared
    #Spark #Gluten #Comet #Velox
  15. Every Apache Spark release, and the problem each one was built to solve
    #Spark #Releases #Migration #Defaults #Internals
  16. Spark Connect: the driver becomes a server, and your application becomes a client
    #SparkConnect #gRPC #Arrow #PySpark
  17. Structured Streaming internals: the checkpoint, the state store, and what a restart cannot change
    #Spark #Streaming #State #Watermarks
  18. Spark Submit Command generator using Iceberg Catalog
    #Spark #Utilities #Iceberg
  19. A Spark JVM playbook: custom logging, class loading, stack size and proxies
    #Spark #Troubleshoot #Logging
  20. Logging a Spark stream: the job you cannot restart to debug
    #Spark #Streaming #Logging #Log4j

Tools 2

  1. Six URL rewrites that turn any GitHub repository into an editor, a prompt, a diagram or an MCP server
    #GitHub #MCP #Tools
  2. Inspecting Parquet files from the command line with parquet-cli
    #Tools #Parquet