Industrial data is no longer scarce. CNC machines, PLCs, robots, sensors, MES, CMMS, energy meters, and quality systems generate massive volumes of signals every second. The real problem is not collection. It is storage, structure, and analysis.

Many factories still rely on fragmented historians, Excel exports, or black-box analytics tied to a single vendor. That approach does not scale. Modern industrial teams need data lake and analytics platforms that can ingest raw OT and IT data, retain it cheaply, and make it usable for analysis, dashboards, ML, and decision making.

What we mean by “industrial analytics & data lake”

In this context, a platform qualifies if it can do most of the following:

• Ingest high-volume time-series and event data
• Store raw and processed data long-term
• Support SQL, time-series, or analytical queries
• Integrate with OT protocols or IIoT pipelines
• Feed dashboards, reports, or ML models

Pure visualization tools alone are excluded. This list focuses on storage + analytics foundations.

1. Apache Druid

Best for: Real-time industrial analytics at scale.

Apache Druid is a high-performance, column-oriented analytics database designed for real-time ingestion and fast aggregations. It is widely used for telemetry, clickstream, and IoT workloads. In manufacturing, Druid works well for:

• OEE analysis across many machines
• Event-driven downtime analytics
• High-cardinality sensor data

It supports real-time ingestion from Kafka and batch ingestion from data lakes.

License: Apache 2.0, open source.

2. ClickHouse

Best for: Fast analytical queries on massive production datasets.

ClickHouse is an open source columnar database optimized for analytics. It is extremely fast and efficient for time-series and event data. Typical industrial use cases:

• Multi-year machine data storage
• Production and quality trend analysis
• Energy and consumption analytics
• MES and IIoT data backends

ClickHouse is increasingly replacing traditional historians in modern stacks.

License: Apache 2.0, open source.

3. Apache Druid + Kafka Stack (Pattern)

Best for: Streaming OT data into analytics in near real time.

While not a single product, this pattern is common in industry. Kafka handles ingestion from gateways, PLCs, and IIoT platforms. Druid consumes streams and makes them queryable within seconds. This stack supports:

• Live downtime root cause analysis
• Real-time KPI tracking
• Event correlation across lines and plants

License: Fully open source components.

4. Apache Hadoop + HDFS (Modernized)

Best for: Long-term industrial data lakes.

Hadoop is no longer trendy, but it still powers many industrial data lakes. With HDFS or object storage (S3-compatible), it stores raw OT data cheaply for years. Used for:

• Historical production analysis
• ML training datasets
• Compliance and traceability archives

Usually paired with Spark, Trino, or Presto for analytics.

License: Apache 2.0, open source.

5. Apache Spark

Best for: Large-scale industrial analytics and ML pipelines.

Spark is a distributed analytics engine rather than a database. It processes data stored in lakes like HDFS, S3, or object stores. In manufacturing it is used for:

• Feature engineering for predictive maintenance
• Batch OEE and quality analysis
• Advanced statistical modeling

Spark shines when datasets are too large for single-node databases.

License: Apache 2.0, open source.

6. Trino (formerly PrestoSQL)

Best for: Unified SQL across industrial data sources.

Trino is a distributed SQL query engine that can query:

• Data lakes
• Time-series databases
• Relational databases
• Object storage

It acts as a federated analytics layer, allowing engineers to analyze MES data, sensor data, and ERP data together without copying everything into one database.

License: Apache 2.0, open source.

7. Apache Pinot

Best for: User-facing industrial analytics applications.

Apache Pinot is designed for low-latency analytics similar to Druid, but optimized for interactive dashboards and applications. Encouraging use cases:

• Production dashboards with sub-second queries
• Machine performance comparisons
• Shift-level KPI analytics

Pinot is well suited when analytics are embedded into applications.

License: Apache 2.0, open source.

8. TimescaleDB (Community Edition)

Best for: Industrial time-series with SQL and retention control.

TimescaleDB extends PostgreSQL into a time-series database. It supports:

• Compression
• Retention policies
• Continuous aggregates

It is a common choice for:

• Machine telemetry
• Energy monitoring
• Maintenance signals

It integrates cleanly with BI tools and Python analytics.

License: Apache 2.0 (community features), open core.

9. QuestDB

Best for: High-ingest industrial time-series data.

QuestDB is a high-performance time-series database built for fast ingestion and SQL querying. Used for:

• High-frequency sensor streams
• Vibration and condition monitoring
• Financial-style tick data adapted to machines

It offers impressive write performance with low operational complexity.

License: Apache 2.0, open source.

10. Apache Superset (Analytics Front End)

Best for: Open source analytics UI over industrial data lakes.

Superset is not a data lake itself, but it is often the analytics layer on top of ClickHouse, Druid, Trino, or PostgreSQL. Used for:

• Production and quality dashboards

• Ad-hoc SQL exploration

• Sharing analytics with non-technical users

It completes the stack by turning raw data into insights.

License: Apache 2.0, open source.

 Free Industrial Analytics and Data Lake Platforms Comparison Table

How industrial teams actually use these stacks

A realistic modern manufacturing analytics stack often looks like this:

• Edge & IIoT: PLCs, CNCs, sensors → MQTT / OPC UA
• Ingestion: Kafka or MQTT brokers
• Storage: ClickHouse or Druid for analytics, object storage for raw data
• Analytics: Spark or Trino for deep analysis
• Visualization: Superset or Grafana

This approach avoids vendor lock-in and keeps raw production data under your control.

Final Thoughts

Industrial analytics is no longer about buying a single “smart factory” platform. It is about assembling the right open foundations that scale with your data and your questions.

The tools listed here power some of the largest data systems in the world. With proper architecture, they work just as well on the shop floor. For manufacturers serious about OEE, energy, quality, and predictive maintenance, an open data lake and analytics platform is no longer optional. It is the backbone.

About MDCplus

Our key features are real-time machine monitoring for swift issue resolution, power consumption tracking to promote sustainability, computerized maintenance management to reduce downtime, and vibration diagnostics for predictive maintenance. MDCplus's solutions are tailored for diverse industries, including aerospace, automotive, precision machining, and heavy industry. By delivering actionable insights and fostering seamless integration, we empower manufacturers to boost Overall Equipment Effectiveness (OEE), reduce operational costs, and achieve sustainable growth along with future planning.