7 posts tagged “Data Lakehouse”
A six-part series on IBM's own answer to the MAS 9 native-analytics ceiling: watsonx.data's open Iceberg lakehouse, fit-for-purpose query engines, MIF/Kafka extraction, medallion architecture, watsonx.ai and RAG, closed-loop write-back, and an honest head-to-head with Databricks.
Surendra KattaJul 19, 2026Part 0 of 6
MAS 9 native analytics has a real ceiling. This post names it, then makes the honest IBM-native case for watsonx.data — open Apache Iceberg, fit-for-purpose engines, and a bridge to watsonx.ai your MAS AI Service license already opened.
Surendra Katta
MAS 9 ships real native analytics — Operational Dashboards, Cognos, Health, Predict, Monitor — but every one of them has a ceiling. Part 1 names those ceilings precisely and explains why a lakehouse, not another dashboard, is what sits past them.
Surendra KattaJul 17, 2026
Four real ways to extract MAS 9 data for a Databricks lakehouse — REST/JSON API, MIF over Kafka and the Data Export framework, DB-direct, and Lakehouse Federation — with real object structures, Kafka broker config, and honest CDC framing.
Surendra KattaJul 17, 2026
A table-by-table medallion architecture for MAS 9 data — cleansing rules for WORKORDER, ASSET, MATUSETRANS, PM, and meter data, conformed dimensions, and gold marts for reliability and cost analytics.
Surendra KattaJul 17, 2026
Worked Databricks SQL for the five gold-layer questions a maintenance leader actually asks: MTBF/MTTR trend health, maintenance cost rollups, slow-moving inventory, work order backlog aging, and PM compliance — built on Part 3's silver and gold tables.
Surendra Katta
A six-part series on where MAS 9 native analytics stops and Databricks starts — data extraction patterns, the medallion architecture for Maximo objects, five concrete analytics use cases, custom ML versus Maximo Predict, and lakehouse governance.
Surendra KattaJul 17, 2026Part 0 of 6