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

Chaos Genius is an open source ML powered analytics engine for outlier detection and root cause analysis. Chaos Genius can be used to monitor and analyse high dimensionality business, data and system metrics at scale. Using Chaos Genius, users can segment large datasets by key performance metrics (e.g. Daily Active Users, Cloud Costs, Failure Rates) and important dimensions (e.g., countryID, DeviceID, ProductID, DayofWeek) across which they want to monitor and analyse the key metrics.

Chaos Genius - ML powered analytics engine for outlier detection and root cause analysis

Kaskada is a unified event processing engine that provides all the power of stateful stream processing in a high-level, declarative query language designed specifically for reasoning about events in bulk and in real time.
Kaskada's query language builds on the best features of SQL to provide a more expressive way to compute over events. Queries are simple and declarative. Unlike SQL, they are also concise, composable, and designed for processing events. By focusing on the event-processing use case, Kaskada's query language makes it easier to reason about when things happen, state at specific points in time, and how results change over time.
Kaskada can be best used for Machine learning, Analytics, Monitoring, Reactive applications.
Features
<ul dir="auto">
<li>Stateful aggregations: Aggregate events to produce a continuous timeline whose value can be observed at arbitrary points in time.</li>
<li>Automatic joins: Every expression is associated with an &ldquo;entity&rdquo;, allowing tables and expressions to be automatically joined. Entities eliminate redundant boilerplate code.</li>
<li>Event-based windowing: Collect events as you move through time, and aggregate them with respect to other events. Ordered aggregation makes it easy to describe temporal interactions.</li>
<li>Pipelined operations: Pipe syntax allows multiple operations to be chained together. Write your operations in the same order you think about them. It's timelines all the way down, making it easy to aggregate the results of aggregations.</li>
<li>Row generators: Pivot from events to time-series. Unlike grouped aggregates, generators produce rows even when there's no input, allowing you to react when something doesn't happen.</li>
<li>Continuous expressions: Observe the value of aggregations at arbitrary points in time. Timelines are either &ldquo;discrete&rdquo; (instantaneous values or events) or &ldquo;continuous&rdquo; (values produced by a stateful aggregations). Continuous timelines let you combine aggregates computed from different event sources.</li>
<li>Native time travel: Shift values forward (but not backward) in time, allowing you to combine different temporal contexts without the risk of temporal leakage. Shifted values make it easy to compare a value &ldquo;now&rdquo; to a value from the past.</li>
<li>Simple, composable syntax: It is functions all the way down. No global state, no dependencies to manage, and no spooky action at a distance. Quickly understand what a query is doing, and painlessly refactor to make it DRY.</li>
</ul>

Kaskada is a unified event processing engine that provides all the power of stateful stream processing in a high-level, declarative query language designed specifically for reasoning about events in bulk and in real time.

Kaskada - Modern, open-source event-processing

KNIME, pronounced [naim], is a modern data analytics platform that allows you to perform sophisticated statistics and data mining on your data to analyze trends and predict potential results. Its visual workbench combines data access, data transformation, initial investigation, powerful predictive analytics and visualization. KNIME also provides the ability to develop reports based on your information or automate the application of new insight back into production systems.

Knime - Data Analytics Platform

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

Kudu is a storage system for tables of structured data. Kudu provides a combination of fast inserts/updates and efficient columnar scans to enable multiple real-time analytic workloads across a single storage layer. As a new complement to HDFS and Apache HBase, Kudu gives architects the flexibility to address a wider variety of use cases without exotic workarounds.

Kudu - Hadoop storage layer to enable fast analytics on fast data

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

DalmatinerDB is a metric database written in pure Erlang. It takes advantage of some special properties of metrics to make some tradeoffs. Its goal is to make a store for metric data (time, value of a metric) that is fast, has a low overhead, and is easy to query and manage. DalmatinerDB allows for metric input in second or even sub-second precision. It will interpolate the missing values to the best of it’s abilities. This is usually acceptable for aggregated data.
 
DalmatinerDB is designed to run on ZFS file system and it relies heavily on taking advantage of ZFS facilities such as the ARC, ZIL, checksums and volume compression. The frontend servers give users access to a rich SQL-like query language that's easy to use for both dev and ops. It integrates nicely with Grafana making it easy to build beautiful dashboards.

DalmatinerDB is a metric database written in pure Erlang. It takes advantage of some special properties of metrics to make some tradeoffs. Its goal is to make a store for metric data (time, value of a metric) that is fast, has a low overhead, and is easy to query and manage. DalmatinerDB allows for metric input in second or even sub-second precision. It will interpolate the missing values to the best of it’s abilities. This is usually acceptable for aggregated data.

DalmatinerDB - Fast distributed metrics database in Erlang

ToroDB helps to transform your NoSQL data from a MongoDB replica set into a relational database in PostgreSQL. There are other solutions that are able to store the JSON document in a relational table using PostgreSQL JSON support, but it doesn't solve the real problem of 'how to really use that data'. ToroDB Stampede replicates the document structure in different relational tables and stores the document data in different tuples using those tables.
 
MongoDB is a great idea, but sooner or later some kind of business intelligence, or complex aggregated queries are required. At this point MongoDB is not so powerful and ToroDB Stampede borns to solve that problem.

ToroDB helps to transform your NoSQL data from a MongoDB replica set into a relational database in PostgreSQL. There are other solutions that are able to store the JSON document in a relational table using PostgreSQL JSON support, but it doesn't solve the real problem of 'how to really use that data'. ToroDB Stampede replicates the document structure in different relational tables and stores the document data in different tuples using those tables.


ToroDB Stampede - Provides better analytics on top of MongoDB and make it easier to migrate from MongoDB to SQL 

EventHub enables companies to do cross device event tracking. Events are joined by their associated user on EventHub and can be visualized by the built-in dashboard to answer the following common business questions.

EventHub - An open source event analytics platform

Superset is fast, lightweight, intuitive, and loaded with options that make it easy for users of all skill sets to explore and visualize their data, from simple line charts to highly detailed geospatial charts. It easily integrates your data, using either our simple no-code viz builder or state of the art SQL IDE. Superset can query data from any SQL-speaking datastore or data engine (e.g. Presto or Athena) that has a Python DB-API driver and a SQLAlchemy dialect.
 
Superset provides: 
<ul>
<li>An intuitive interface for visualizing datasets and
crafting interactive dashboards</li>
<li>A wide array of beautiful visualizations to showcase your data</li>
<li>Code-free visualization builder to extract and present datasets</li>
<li>A world-class SQL IDE for preparing data for visualization, including a rich metadata browser</li>
<li>A lightweight semantic layer which empowers data analysts to quickly define custom dimensions and metrics</li>
<li>Out-of-the-box support for most SQL-speaking databases</li>
<li>Seamless, in-memory asynchronous caching and queries</li>
<li>An extensible security model that allows configuration of very intricate rules on
on who can access which product features and datasets.</li>
<li>Integration with major
authentication backends (database, OpenID, LDAP, OAuth, REMOTE_USER, etc)</li>
<li>The ability to add custom visualization plugins</li>
<li>An API for programmatic customization</li>
<li>A cloud-native architecture designed from the ground up for scale</li>
</ul>

Superset is fast, lightweight, intuitive, and loaded with options that make it easy for users of all skill sets to explore and visualize their data, from simple line charts to highly detailed geospatial charts. It easily integrates your data, using either our simple no-code viz builder or state of the art SQL IDE. Superset can query data from any SQL-speaking datastore or data engine (e.g. Presto or Athena) that has a Python DB-API driver and a SQLAlchemy dialect.

Apache Superset is a Data Visualization and Data Exploration Platform 

Polr is an intrepid, self-hostable open-source link shortening web application with a robust API. It allows you to host your own URL shortener, to brand your URLs, and to gain control over your data. Polr is especially easy to use, and provides a modern, themable feel.

Polr - A modern, powerful, and robust URL shortener

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