Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

IoT sensors, financial market ticks, smart meters and infrastructure telemetry generate relentless streams of append-only timestamped records. Stored in standard relational tables, this volume quickly overflows B-Tree indexes out of RAM, collapses insert throughput and drives up storage bills.

This course covers time-series workload characteristics, hands-on architecture and querying with TimescaleDB (hypertables, chunk sizing, columnar compression, continuous aggregates) alongside purpose-built engines (InfluxDB 3 / VictoriaMetrics / QuestDB), preventing high-cardinality explosions, time-windowed analytics (time_bucket, ASOF JOIN, gapfilling), and automated downsampling and retention tiers.

Who Should Attend?

  • Backend and data engineers building IoT, telemetry, energy, industrial (IIoT) or financial market platforms
  • DevOps, SRE and platform engineers designing metric storage and observability backends
  • Database teams struggling with slow queries and runaway storage costs on timestamped tables
  • Prerequisite: working knowledge of SQL and core database concepts

Key Takeaways

  • Choose objectively between PostgreSQL-based TimescaleDB and purpose-built engines (InfluxDB, VictoriaMetrics, QuestDB)
  • Model tags/metadata vs fields/metrics properly to prevent high-cardinality index bloat
  • Configure TimescaleDB hypertable chunk intervals, native columnar compression and Continuous Aggregates
  • Write expressive time-series queries with time_bucket, gapfilling/LOCF interpolation and ASOF JOIN
  • Automate multi-tier downsampling (rollups) and chunk-drop retention policies

Curriculum

Time-Series Workloads and TSDB Architectural Comparison
  • Characteristics of time-series data: append-heavy ingestion, recency-biased reads and lifecycle ageing
  • Why standard B-Tree indexes struggle at scale, and how time-chunking and columnar/TSM engines solve it
  • Decision matrix: TimescaleDB, InfluxDB (v2 vs v3 Apache Arrow/Parquet), VictoriaMetrics and QuestDB
Schema Modelling, High Cardinality and High-Throughput Ingestion
  • Narrow vs Wide table schemas and joining relational device/asset metadata
  • Understanding High Cardinality: why unbounded IDs in indexed tags blow up memory
  • Batch sizing, handling out-of-order / late-arriving samples and ingestion backpressure
TimescaleDB: Hypertables, Columnar Compression and Time Analytics
  • Creating Hypertables, sizing chunk_time_interval to fit active chunks in RAM, and composite indexes
  • Time-series hyperfunctions: time_bucket, time_bucket_gapfill, locf, interpolate, first() / last() and candlesticks
  • Hybrid row/columnar storage: configuring segmentby and orderby for 90%+ compression on aged chunks
The InfluxDB / VictoriaMetrics Ecosystem and Telemetry Queries
  • Measurements, Tag Sets, Field Sets, Timestamps and ingesting via Line Protocol
  • Querying with InfluxDB 3 (SQL/InfluxQL) and PromQL/MetricsQL for rates, derivatives and percentiles
  • Ingestion pipelines with Telegraf, OpenTelemetry Collector and Kafka consumers
Continuous Aggregates, Downsampling and Retention Policies
  • Accelerating Grafana dashboards with real-time incremental Continuous Aggregates
  • Multi-resolution rollups (e.g. 7-day raw data, 90-day 1-minute rollups, 2-year hourly summaries)
  • Instantaneous data expiry via drop_chunks and automated retention policies instead of expensive DELETEs

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