What's the most cost-effective way to scale telemetry analytics using an ops data lake for a growing constellation?

The most cost-effective way is usually to separate ingestion, storage, and query paths, and to treat the ops data lake as the system of record while pushing only the highest-value/lowest-latency data into expensive analytics tiers.

Practical pattern

1) Land everything in object storage first

Use a cheap, durable lake format:

• S3 / GCS / Azure Blob
• Store as Parquet or ORC
• Partition by:
  • time
  • spacecraft / asset ID
  • telemetry type / subsystem

This keeps raw telemetry cheap to retain and replay.

2) Use a streaming buffer only for near-real-time needs

For live dashboards, alerts, and anomaly detection:

• Kafka / Redpanda / Kinesis / Pub/Sub
• Keep retention short
• Don’t use the stream as the long-term store

This avoids paying streaming-system costs for historical analytics.

3) Build a lakehouse on top

Use open table formats:

• Apache Iceberg
• Delta Lake
• Hudi

Why:

• efficient incremental reads
• schema evolution
• compaction
• partition pruning
• avoids expensive rewrites as the constellation grows

For telemetry, this is usually much cheaper than a traditional warehouse-only approach.

4) Tier your analytics workloads

Split workloads by latency and cost:

• Hot path

  • alerts
  • ops dashboards
  • recent flight data
  • small indexed store or OLAP engine

• Warm path

  • engineering investigations
  • trend analysis
  • batch anomaly detection
  • query directly from the lake with Trino/Presto/Spark

• Cold path

  • archive and compliance
  • compress aggressively
  • infrequent access

5) Use columnar + compression aggressively

Telemetry is often highly compressible. Best practices:

• Parquet with ZSTD or Snappy
• avoid tiny files
• compact regularly
• write in larger batches
• normalize repeated fields where possible

6) Keep compute ephemeral and elastic

Instead of always-on clusters:

• serverless SQL/query engines
• auto-scaling Spark
• spot/preemptible instances for batch jobs
• ephemeral notebooks or jobs for investigations

This is often the biggest cost saver at scale.

7) Optimize for access patterns, not just storage

For a growing constellation, costs blow up from scanning too much data. Use:

• partitioning by time and vehicle
• clustering/sorting on common filters
• materialized views for fleet-level KPIs
• pre-aggregated rollups for standard reports

8) Push down filtering early

Do not pull raw telemetry into downstream tools unless necessary. Use:

• predicate pushdown
• projection pruning
• approximate aggregations where acceptable
• edge-side summarization if bandwidth is constrained

Recommended reference architecture

Telemetry sources → stream bus for live events → object storage lake as canonical store → Iceberg/Delta tables →

• Trino/Spark for batch analytics
• OLAP store for fast dashboards
• ML/anomaly jobs on-demand
• archive tier for old data

If you want the cheapest robust stack

A common low-cost combo is:

• Object storage
• Parquet
• Iceberg
• Trino
• Spark or DuckDB for ad hoc
• Kafka/Redpanda only for live ingestion
• serverless or spot-based compute

Biggest cost traps to avoid

• using a warehouse for all raw telemetry
• keeping streaming infrastructure as long-term storage
• too many small files
• over-partitioning
• constant full-table scans
• always-on compute clusters
• duplicating data into many analytics systems

Short answer

The most cost-effective approach is to store raw telemetry cheaply in a columnar lakehouse, use a short-retention stream for real-time operations, and run scalable elastic compute only when needed. That gives you low storage cost, low operational overhead, and room to grow with the constellation.

If you want, I can also give you:

1. a reference architecture diagram in text,
2. a vendor-neutral stack recommendation, or
3. a cost model for small / medium / large constellation telemetry volumes.