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Drinking from the “Data Fire Hose”

A data firehose, or a data stream, is a continuous, high-volume flow of data that is produced by a data source. Common data firehoses include social media feeds, financial market data, and transponder data.

High volume data streams are found in a variety of use-cases, including real-time analytics, fraud detection, and event-driven architectures.

Data firehoses can be challenging to work with due to their high volume and rate of change. They often require specialized software and infrastructure to process and analyze data in a timely manner.

One common way to bring together many data feeds is to use a data streaming platform,

There are several layers of tools that are typically used to work with data firehoses, including:

  1. Data ingestion layer:
    These technologies are used to capture and collect data from various sources and feed it into the data firehose. Examples include APIs, data collectors, and messaging systems.
  2. Data streaming layer:
    These technologies are used to combine and do primary processing of data. Common tools include Apache Kafka or AWS Kinesis. These platforms allow you to ingest and process data streams and do basic analytics in real time, and can scale to handle very large volumes of data.
  3. Data storage layer:
    These technologies are used to store and manage the data that is captured by the data firehose. Common examples have included databases, such as NoSQL databases (e.g., MongoDB, Cassandra) or column-oriented databases (e.g., HBase, Parquet), and data lakes, such as Amazon S3 or Hadoop HDFS.
  4. Data visualization and reporting layer:
    These technologies are used to visualize and report on the data that is captured and analyzed by the data firehose. Examples include dashboarding tools, such as Tableau or Google Data Studio.

Kinetica is designed to handle data ingestion, stream processing, data storage, and visualization all in a single platform, making it a powerful tool for working with data firehoses.

It uses a distributed in-memory architecture and a GPU-accelerated database to enable fast data ingestion and processing, and it includes a range of visualization and reporting tools for creating interactive dashboards and reports.

Some examples of how Kinetica could be used as a data firehose include:

  • Capturing and analyzing real-time data from IoT devices or sensors
  • Processing and analyzing real-time data from social media or financial markets
  • Analyzing and visualizing real-time data from logistics or supply chain operations

Overall, Kinetica is ideal for organizations that need to capture, process, and analyze large volumes of data in real-time, and that want to do so using a single, integrated platform.

You can Try Kinetica for free. Kinetica Cloud includes several example workflows that show you how to connect to data feeds, enrich them to gain insights, reporting and alerts.

After data has been enriched, Kinetica’s Gold Certified Kafka connector works both ways enabling you to use Kinetica as a source for real-time enriched feeds.

If you’d like to branch out, there are a number public high-volume data feeds that can be accessed with Kinetica. Here are a few options:

  1. Apache Kafka itself provides a number of sample data feeds that can be used for testing and experimentation. These feeds can be found in the Apache Kafka GitHub repository.
  2. The US Securities and Exchange Commission (SEC) provides a real-time feed of market data for all publicly traded companies. This feed, known as the Consolidated Audit Trail (CAT), can be accessed via a Kafka topic.
  3. The New York Stock Exchange (NYSE) also provides a real-time feed of market data that can be accessed via Kafka. This feed includes data on all NYSE-listed stocks, as well as data on options, ETFs, and other financial instruments.
  4. There are also a number of commercial providers that offer high-volume data feeds that can be accessed via Kafka. These providers often offer a variety of data sources, including financial market data, social media data, and IoT data.
  5. Automatic Identification System (AIS) data is a valuable resource for tracking the movement and behavior of vessels at sea. There are also a number of commercial providers that offer AIS data feeds. These providers often offer a variety of data sources, including global coverage and real-time data feeds. Some national and regional authorities make AIS data available to the public. For example, the United Kingdom Hydrographic Office (UKHO) provides a free AIS data feed that covers UK waters, and the European Maritime Safety Agency (EMSA) provides AIS data through its open data portal.

The availability and quality of AIS data can vary depending on the source. Some sources may require a subscription or a fee to access the data, and it is always a good idea to carefully review the terms of use before accessing any data feed.

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