One Lakehouse.
SQL, Streaming, ML.
No ETL Tax.
DataLynxr runs all three workload types against the same storage layer — so your data team stops copy-jobbing and starts shipping.
Three Workloads. One Storage Layer.
Other tools make you choose. DataLynxr runs SQL, streaming, and ML against the same lakehouse tables — no copies, no sync, no drift.
ANSI SQL Directly on Your Lake
Run ANSI SQL directly on your S3/GCS/ADLS data. No warehouse copy. Vectorized engine, window functions, sub-second P95 on 10TB datasets.
Your analysts query the same tables your pipelines write to. Zero ETL middleware required.
- Full ANSI SQL with window functions, CTEs, EXPLAIN plans
- Vectorized query engine — sub-second P95 at 10TB scale
- Delta Lake / Apache Iceberg / Hudi table format support
- Direct S3, GCS, ADLS connectivity — no data copy
Direct Kafka → Lakehouse Tables
Ingest Kafka, Kinesis, or Pulsar directly into lakehouse tables. Exactly-once semantics. Sub-5s end-to-end latency.
The same tables your analysts query — no landing zone, no staging, no batch bridge.
- Kafka, Kinesis, Pulsar source connectors
- Exactly-once delivery guarantee
- Sub-5 second end-to-end latency
- ACID transactions on streaming tables
Train and Serve from the Same Table
Serve ML features from the same tables your SQL and streaming jobs write. Point-in-time correct joins for training. No feature store duplication, no training/serving skew.
Time-travel semantics give you historical accuracy without a separate feature store.
- Point-in-time correct joins for training datasets
- Same lakehouse tables for training and serving
- Python SDK with
get_feature_values(timestamp=T) - Zero training/serving skew by design
Connect, Query, Ship
Connect your storage
Point DataLynxr at your existing S3, GCS, or ADLS bucket. No data copy. No migration. Your data stays where it is.
Run any workload
SQL queries, streaming ingestion, ML feature serving — pick one or all three. Same lakehouse tables, same ACID guarantees.
Ship faster
Dashboards, real-time apps, model inference — all from one source. No pipeline to maintain. No sync to debug.
What data teams say
"We had six Airflow DAGs moving 800 GB/day from S3 Iceberg tables into Snowflake just so our analysts could query them. We shut down all six and pointed DataLynxr at the same S3 bucket. TPC-DS Q47 that used to take 12s in Snowflake runs in 3.9s against the Iceberg tables directly. Our monthly cloud bill dropped by roughly $3,200."
"We had a Kafka → S3 landing zone → Glue ETL → Redshift pipeline that produced 4-hour-old data. The Glue jobs failed 2–3 times a week. We replaced the whole chain with a single DataLynxr streaming connector — Kafka topic to Delta table, P99 latency under 4 seconds. Our 38 real-time dashboards went from 4-hour refresh to live."
"We were running a Tecton feature store alongside our Iceberg lakehouse — same features computed twice, in two places, with two different code paths. Training/serving skew was a constant oncall item. DataLynxr's point-in-time reads from the same Iceberg table replaced both. We retired the Tecton deployment and one Spark streaming job. Skew incidents: zero in the past 6 months."
Pick your starting point
SQL Analytics
Run ANSI SQL directly on your lakehouse storage. No warehouse copy, no egress bill, no 4-hour refresh cycle.
ExploreStreaming Ingestion
Ingest Kafka and Kinesis directly into Delta tables. Exactly-once semantics. Sub-5s end-to-end latency.
ExploreML Feature Store
Serve training and inference features from the same table. Point-in-time correct. No separate feature store needed.
ExploreReady to cut your ETL overhead?
Join data engineering teams running SQL, streaming, and ML on the same lakehouse.