Columnar vs Row-based storage
row-based storage stores the entire records together, making it efficient for transactional workloads that frequently access complete rows.Optimal for oltp where you typically need all columns of specific records.
Columnar storage groups data by columns, enabling efficient compression and fast analytical queries that only access specific columns.ideal for olap workloads, data warehousing, and scenarios with selective column access patterns.
Partitioning
Divides large datasets into smaller, managealbe segments based on specific criteria like data ranges, geogrphic regions or hash values. this improves query performance and enables parallel processing and simplifies data management tasks like archiving and backup.
CAP theorem
states that distributed systems can guarantee at most two of the three properties, consistency-all nodes see the same data, availability-system remains operation and partition torelance-system continues despite network failure.Modern systems often provide tunable consisitency levels, allowing different guarantees for different use cases within the same system
windowing in streaming
divides continuous data streams into finite chunks for processing, tumbling windows are fixed-size, non-overlapping time intervals.Sliding windows overlap and move continuously. session windows group events based on activity periods with gaps indicating session boundaries
inludes handling late-arriving data, watermark for determining window completeness and trigger conditions for window evaluation.system like apache flink and kafka streams provide sophisticated windowing capabilities with configurable lateness and results updating strategies.proper windowing enbales meaningful aggregations and analysis over unbounded data streams while managing memory usage and computational complexity.
Retry logic & Dead letter queues
retry logic automatically reattempts failed operations with strategies like exponential backoff, fixed delays, or linear backoff. it handles transient failures and must be implemented carefully to avoid overwhelming systems or creating infinite loops
DLQs capture messages that cannot be processed after exhausting retry attempts. they prevent message loss, enable failure analysis and allow for manual intervention or atlernatives.eg categorizing errors (transient vs permanent), implementing circuit breakers, adding jitter to prevent thundering herd problems, and monitoring retry patterns to identify systemic issues requiring architectural changes.
Backfilling and reprocessing
processes historical data to populate new datasets or fill gaps in existing ones. common when introducing new features, fixing data quality issues or migrating systems, backfill jobs often process data in reverse chronological order to provide recent data first.
reprocessing reruns data pipelines on existing data, typically after fixing bugs, updating business logic, or recovering from failures. it requires carefull consideration of downstream impacts and often involves versioning strategies to manage different data generations
Challenges include managing computational resources, ensuring data consistency during the process, handling schema evolution, and coordinating with downstream consumers to prevent conflicts or inconsistencies.
Time travel & data versioning
Time travel allows quering historical versions of data, enabling analysis of changes over time and recovery from accidental modifications. systems like snowflake, bigquery and delta lake provide built-in time travel capabilities with configurable retention periods
data versioning tracks changes of datasets and schemas, similar to version control for code. it enables reproducible analytics, A/B testing of data transformations and rollback capabilities when issues are discovered.
Implementation approaches include snapshot-based versioning, log-based change tracking, and copy-on-write mechanisms. These features are crucial for data debugging, compliance auditing, and maintaining data science experiment reproducibility.
Distributed processing concepts
Distributed processing enables handling large-scale data by spreading computation across multiple machines. Key concepts include data locality (processing data where it's stored), fault tolerance through replication and checkpointing, and coordination mechanisms for task distribution.
Frameworks like Apache Spark use concepts such as resilient distributed datasets (RDDs), lazy evaluation for optimization, and automatic task scheduling. Map-reduce paradigms break complex operations into parallelizable steps, while more modern frameworks support iterative algorithms and real-time processing.
Challenges include managing data shuffling costs, handling stragglers (slow tasks), ensuring fault tolerance, and optimizing resource utilization across the cluster while maintaining data consistency and system reliability.
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