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THE DAILY INDEX

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DEEP DIVES

TLDR Data

TIMESERIES TIERED STORAGE JOURNEY: KAFKA/FLINK STREAMS TO NATIVE CASSANDRA COLD READS

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.

GEM TRAINING: HOW META DOUBLED THE EFFICIENCY OF ITS LLM-SCALE ADS FOUNDATION MODEL

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.

BRINGING DUCKLAKE TO DATAFUSION

DuckLake is now implemented for Apache DataFusion as a production-ready, open-source catalog backend, giving Hotdata transactional metadata, snapshots, and schema management for Parquet tables in object storage. The design supports multiple relational catalog backends and logical catalogs, enabling millions of isolated, ephemeral databases for AI agents without duplicating infrastructure. TPC-H benchmarks across 22 queries and three dataset sizes showed no measurable overhead versus direct Parquet access, with slightly better performance due to less metadata discovery during query planning.

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