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<div style="display: none; max-height: 0px; overflow: hidden;">Netflix’s TimeSeries Abstraction stores and queries multi-petabyte temporal datasets, tiering older, immutable slices from hot Cassandra </div>
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<h1><strong>TLDR Data <span id="date">2026-08-06</span></strong></h1>
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<h1><strong>Deep Dives</strong></h1>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fnetflixtechblog.medium.com%2Ftimeseries-tiered-storage-journey-kafka-flink-streams-to-native-cassandra-cold-reads-e59d597c9d60%3Futm_source=tldrdata/1/0100019fd68dca1b-ec071493-70e1-4e97-a650-f0afbcfa80fc-000000/DTFhkFiHI-mVdEoQXNaYAG5pjXFTuqS1fkwJfBXgFEk=452">
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<strong>TimeSeries Tiered Storage Journey: Kafka/Flink Streams to Native Cassandra Cold Reads (6 minute read)</strong>
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Netflix's TimeSeries Abstraction stores and queries multi-petabyte temporal datasets, tiering older, immutable slices from hot Cassandra into cheaper S3 storage. The earlier design used Kafka and Flink to stream Parquet to S3 and daily compaction, serving cold reads with roughly 500 ms p99 latency, but added operational cost. The later Cassandra-native cold tier reads directly from S3 backups, serves 15+ PB of compressed cold data, and improves p90 latency by about 30% while preserving the same query API.
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Meta scaled its ads recommendation foundation model 4x in 12 months and doubled training efficiency to 20–25% MFU through five techniques: jagged flash attention to eliminate padding waste, a unified attention kernel with 2–3.5x speedup, MXFP8 precision training, SM-free collective communication that reclaims ~23 SMs, and a load-balancing shuffle that adds 4% efficiency at zero communication cost.
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<strong>LLMs for Relevance: Automating High-Quality Product Relevance Labeling in Flipkart Search (10 minute read)</strong>
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Defining metrics once in a semantic layer reduces the risk of conflicting numbers across Tableau, Power BI, and notebooks, and makes permission controls and change propagation auditable from a single place. The semantic layer is an operational risk mitigation strategy rather than an optional layer.
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<strong>Prototype on a laptop, scale to 16 billion rows: one Polars query (14 minute read)</strong>
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Polars lets teams develop ETL pipelines locally on a representative data sample, then run the exact same LazyFrame queries across 16 billion rows in the cloud without rewriting the logic. The pipeline pre-aggregates raw Polymarket data into small Parquet artifacts in S3, allowing a Plotly Dash dashboard to remain fast without scanning the full dataset on each request.
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<strong>The Mean Means Nothing (9 minute read)</strong>
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A web service latency rollout shows the mean rising 9%, the median falling 46%, and p99 rising 119% within the same dataset, demonstrating how single-metric summaries can hide bimodal distributions. Further analysis like density plots, CDFs, shift functions, and ridgelines provide complementary views that together identify response size as the root cause.
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pyhctsa is a Python toolkit for extracting hundreds of statistical and structural features from single or multiple time series, with support for custom feature sets and variable-length inputs. It also supports direct operation calls and parallel processing across local CPU cores for faster analysis.
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Encoding improves analytical query execution, while compression mainly reduces storage. Dictionary encoding and frame-of-reference enable fast comparisons, pruning, and SIMD operations. CedarDB's results suggest always encoding, then adding zstd only when the disk savings justify it.
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