Apache Unomi (pronounced "You know me") is a Java Open Source customer data platform, a Java server designed to manage customers, leads and visitors data and help personalize customers experiences while also offering features to respect visitor privacy rules (such as GDPR).
 
Apache Unomi is a REST server that manages user profiles and events related to the profiles. It can be used to integrate personalization and profile management within very different systems such as CMS, CRMs, Issue Trackers, native mobile application. It was designed to be easy to integrate with external systems, promoting profile sharing and re-use in very different applications.
 
Basically the server tracks users using different mechanisms (by default cookies), builds a progressively populated profile and associates events that are performed by a user with his profile. Events may range from a click on a page, to a file being downloaded, a native mobile application button being clicked, or anything that can be sent to the server.
 
As Apache Unomi is built as an OSGi application running inside Apache Karaf, it is extremely extensible and built to be scalable. For example it is possible to plugin new conditions, actions, or any other service that may be needed such as beacon tracking or push notifications. Apache Unomi is also the reference implementation of the upcoming OASIS Customer Data Platform specification to help standardize personalization of online experience while promoting ethical web experience management and increased user privacy controls.

Apache Unomi (pronounced "You know me") is a Java Open Source customer data platform, a Java server designed to manage customers, leads and visitors data and help personalize customers experiences while also offering features to respect visitor privacy rules (such as GDPR). 

Apache Unomi - The Open Source Customer Data Platform

Apache StreamPipes is a self-service (Industrial) IoT toolbox to enable non-technical users to connect, analyze and explore IoT data streams. It can Integrate data sets and data streams using the built-in StreamPipes Connect library with support for generic protocols such as HTTP, Kafka, MQTT, OPC-UA, Files or specific adapters for open data sources.
 
It enables flexible modeling of stream processing pipelines by providing a graphical modeling editor on top of existing stream processing frameworks. It empowers non-technical users to quickly define and execute processing pipelines based on an easily extensible toolbox of data sources, data processors and data sinks. StreamPipes has an exchangeable runtime execution layer and executes pipelines using one of the provided wrappers, e.g., standalone or distributed in Apache Flink.

Apache StreamPipes is a self-service (Industrial) IoT toolbox to enable non-technical users to connect, analyze and explore IoT data streams. It can Integrate data sets and data streams using the built-in StreamPipes Connect library with support for generic protocols such as HTTP, Kafka, MQTT, OPC-UA, Files or specific adapters for open data sources.

Apache StreamPipes - A self-service (Industrial) IoT toolbox to enable non-technical users to connect, analyze and explore IoT data streams

Lens provides an Unified Analytics interface. Lens aims to cut the Data Analytics silos by providing a single view of data across multiple tiered data stores and optimal execution environment for the analytical query. It provides a simple metadata layer which provides an abstract view over tiered data stores.

Lens - Unified Analytics interface

ConcourseDB is a distributed self-tuning database with automatic indexing, version control and ACID transactions. ConcourseDB provides a more intuitive approach to data management that is easy to deploy, access and scale while maintaining the strong consistency of traditional database systems.

ConcourseDB  - Self-tuning database designed for both transactions and ad hoc analytics across time

Apache arrow is an open source, low latency SQL query engine for Hadoop and NoSQL. Arrow enables execution engines to take advantage of the latest SIMD (Single input multiple data) operations included in modern processors, for native vectorized optimization of analytical data processing. Columnar layout of data also allows for a better use of CPU caches by placing all data relevant to a column operation in as compact of a format as possible.

Apache Arrow - Powering Columnar In-Memory Analytics 

AsterixDB is a BDMS (Big Data Management System) with a rich feature set that sets it apart from other Big Data platforms. Its feature set makes it well-suited to modern needs such as web data warehousing and social data storage and analysis. It is a highly scalable data management system that can store, index, and manage semi-structured data, but it also supports a full-power query language with the expressiveness of SQL (and more). 
 
AsterixDB can exploit its knowledge of data partitioning and the availability of indexes to avoid always scanning data set(s) to process queries

AsterixDB is a BDMS (Big Data Management System) with a rich feature set that sets it apart from other Big Data platforms. Its feature set makes it well-suited to modern needs such as web data warehousing and social data storage and analysis. It is a highly scalable data management system that can store, index, and manage semi-structured data, but it also supports a full-power query language with the expressiveness of SQL (and more). 

AsterixDB -  Big Data Management System (BDMS)

Dremio is a self-service data platform that empowers users to discover, curate, accelerate, and share any data at any time, regardless of location, volume, or structure. Modern data is managed by a wide range of technologies, including relational databases, NoSQL datastores, file systems, Hadoop, and others. Many of the newer datastores are often more agile and provide improved scalability, but at a cost to speed and ease of access via traditional SQL-based analysis tools. Additionally, raw data found in these stores is often too complex or inconsistent for analysis to use with business intelligence tools.
 
ETL pipelines that load a subset of data into relational databases provide one answer, but aside from the burden these solutions impose on data engineers and IT staff, data becomes stale by the time it is available to analysts and data scientists.

Dremio is a self-service data platform that empowers users to discover, curate, accelerate, and share any data at any time, regardless of location, volume, or structure. Modern data is managed by a wide range of technologies, including relational databases, NoSQL datastores, file systems, Hadoop, and others. Many of the newer datastores are often more agile and provide improved scalability, but at a cost to speed and ease of access via traditional SQL-based analysis tools. Additionally, raw data found in these stores is often too complex or inconsistent for analysis to use with business intelligence tools.

Dremio - The missing link in modern data

Apache Edgent is a programming model and micro-kernel style runtime that can be embedded in gateways and small footprint edge devices enabling local, real-time, analytics on the continuous streams of data coming from equipment, vehicles, systems, appliances, devices and sensors of all kinds (for example, Raspberry Pis or smart phones).  Apache Edgent provides efficient and timely analytics across the whole IoT ecosystem: from the center to the edge.

Apache Edgent - Accelerating Analytics at the Edge

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Apache Unomi - The Open Source Customer Data Platform

Apache StreamPipes - A self-service (Industrial) IoT toolbox to enable non-technical users to connect, analyze and explore IoT data streams

Lens - Unified Analytics interface

ConcourseDB - Self-tuning database designed for both transactions and ad hoc analytics across time

Apache Arrow - Powering Columnar In-Memory Analytics

AsterixDB - Big Data Management System (BDMS)

Dremio - The missing link in modern data

Apache Edgent - Accelerating Analytics at the Edge

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