AI Data Infrastructure

The full data-platform library

The AI-ready pipelines, lakehouse and serving layer your models actually run on — ingested, modelled, tested, governed and observable end to end.

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techniques

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disciplines

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capabilities

the whole spectrum

batch → real-time / AI-native · not just the fashionable end

39 batch58 cloud-native52 real-time / AI-native

/ browse

The 5 capabilities, in depth

Pick a capability — its plain-English job, the questions it answers, and the full toolbox scroll alongside.

the colour is the tierbatchcloud-nativereal-time / AI-native
01

Ingest & stream

get every source in, reliably

33techniques · 2 disciplines
9 batch13 cloud-native11 real-time / AI-native

Land every source — apps, databases, files and events — into your platform on time and without loss, whether it arrives nightly in bulk or as a live stream.

answers questions like

  • →“Are we pulling from all our systems, or is half the business still in spreadsheets?”
  • →“How long after something happens in the source can we see it — hours, or seconds?”
  • →“When a source system changes shape, does our data break silently?”

the toolbox — 33 techniques, batch to real-time / AI-native

Batch & CDC ingestion

18 techniques

Scheduled batch / bulk loadETL extract jobsSFTP / file dropFull table snapshotsJDBC / ODBC pullsREST / API pollingLog-based CDC (Debezium)Airbyte connectorsFivetran connectorsMeltano / Singer tapsSchema-drift handlingIncremental / watermark loadsBackfill + reconciliationStreaming CDC into lakehouseSnowpipe / auto-ingestDatabricks Auto LoaderSchema-registry-driven evolutionIdempotent upserts / MERGE

Streaming & events

15 techniques

Message queues (RabbitMQ / SQS)Micro-batch loadingWebhook captureApache KafkaAmazon KinesisApache PulsarGoogle Pub/SubKafka ConnectConfluent Schema RegistryApache FlinkSpark Structured StreamingKafka Streams / ksqlDBExactly-once semanticsEvent-time watermarks & windowingStateful stream processing

/ the point

Models are only as good as the data under them

Poor data quality costs organisations around $12.9M a year on average (Gartner) — we build the observable, governed foundation so your AI runs on data you can actually trust.

Back to the service

Feature / model serving and model drift live with AIOps / MLOps · The models that sit on top come from Data Science & ML · Migrating legacy pipelines and stacks is AI Modernisation