Data-Engineering

Overview Batch processing collects data over a period and runs computation on the whole bounded set at once, while stream processing handles each event as it arrives, continuously. The choice determines whether your system optimizes for throughput and simplicity or low latency on fresh results. Comparison Diagram BatchStreamData accumulatesinto a bounded setScheduled jobprocesses all at onceResult: high latencyEvents flow continuouslyprocessprocessprocessprocessResult: low latency Comparison Table Aspect Batch Processing Stream Processing Data ingestion Data collected and stored until job triggers Events consumed individually as they arrive Data scope per run Bounded, finite dataset (a file, a partition, a day’s data) Unbounded, continuous sequence of events Processing trigger Scheduled interval or manual kickoff (hourly, nightly) Continuous, triggered by each event or micro-window Latency to result Minutes to hours, depending on schedule Milliseconds to seconds State management Recomputed fresh from full dataset each run Maintained incrementally across the event stream Fault recovery Rerun the failed job against the same input Checkpointing and replay from an offset in the log Ordering guarantees Whole dataset available, so ordering enforced within the job Ordering must be explicitly handled (per-key, watermarks) Resource usage pattern Spiky: idle, then a burst of compute at run time Steady, sustained compute and memory footprint Key Differences Batch operates on a bounded dataset, stream operates on an unbounded sequence of events Batch trades latency for simplicity and throughput; stream trades complexity for freshness Stream systems need watermarks to handle late or out-of-order events, which batch avoids entirely Failure recovery in batch means rerunning the job; stream relies on checkpoint and replay semantics Batch pipelines are easier to reason about and test since input is fixed and reproducible When to Use Each Batch Processing ...

Overview Lambda Architecture and Kappa Architecture are two patterns for building big-data pipelines that need both real-time and historical results. Lambda runs parallel batch and speed layers that get merged at query time, while Kappa pushes everything through a single, replayable stream pipeline. The choice determines how much duplicate logic you maintain and how reprocessing actually works. Comparison Diagram Lambda ArchitectureKappa ArchitectureData SourceBatch Layerfull recomputeSpeed Layerrecent, approximateServingLayerreprocess = rerun batch jobEvent LogStream ProcessingLayersingle codebasereprocess = replay the logqueryquery Comparison Table Aspect Lambda Architecture Kappa Architecture Ingestion path Raw events are forked to both a batch store and a stream processor at once Raw events are written once to an immutable, replayable log (e.g. Kafka) Processing model Two independent codebases — a batch job and a stream job — implement the same logic twice One stream-processing codebase handles both real-time and historical computation Historical reprocessing The batch layer periodically recomputes results over the entire raw dataset Reprocessing means replaying the log from an earlier offset through the same stream job State and storage Separate batch views and speed views are maintained independently, often in different stores A single serving store is continuously updated by the stream processor Result merging The query layer merges or reconciles batch and speed views at read time No merge step — the stream processor’s output is the only view Consistency behavior Speed layer results are approximate until the batch layer overwrites them, so the two can disagree temporarily One computation path avoids batch/speed drift, but correctness depends entirely on the stream engine’s guarantees Operational overhead Higher — two parallel pipelines to build, deploy, and monitor, with duplicated logic Lower pipeline count, but requires a log system with long enough retention to support full replays Best-fit scenario Batch and speed logic genuinely differ, or the org already has mature batch infrastructure Team wants one canonical pipeline and has a stream engine that can absorb both live and replay traffic Key Differences Lambda splits ingestion across a batch layer and a speed layer, while Kappa sends everything through one stream. Reprocessing history in Lambda means rerunning a full batch job; in Kappa it means replaying the log through the same stream code. Lambda’s query layer must reconcile two separate views; Kappa exposes a single serving store with no merge step. Kappa’s design hinges on a durable, long-retention event log capable of full replays, which Lambda does not require. When to Use Each Lambda Architecture ...