Streaming analytics enables real-time logging, processing, and analyzing of data and events from streaming sources. This is essential in industries that consistently rely on real-time information, such as news corporations, financial companies, investment houses, and utility management companies.
Streaming analytics tools continuously run queries to gain information and perform actions on real-time data. When choosing a streaming analytics tool, here are some features to look out for:
Machine learning capabilities: Employ machine learning training models on real-time data streams to make continuous and accurate forecasts. Machine learning is also used to filter and enhance streamed data, and to help visualize collected data and generated predictions. In addition, you can utilize machine learning to help detect anomalies in your current data by comparing it with previously logged data.
SQL support: Continuous SQL queries perform powerful transformations on streaming data, saving data analysts a substantial amount of time and effort. SQL queries also enable users to detect events in real time by running queries from current streams against queries from past event streams.
User-friendly dashboards: Your streaming analytics tool should allow you to create operational dashboards with interactive charts and graphs that provide real-time monitoring and enable data transformation and alert configuration. You should also be able to create role-based user interfaces for different groups within your organization.
It is important that you have a graphical user interface of all data streams and data relationships with information on the streamed data’s origin, destination, and transformation history.
Support for multiple programming languages: Programmers and data analysts should be able to utilize code to automate the data streaming processes and execute various functions on the streamed data using any popular programming language, such as Python, Java, C#, Ruby, and JavaScript.
Rapid scaling: The ability to instantly scale your applications is essential for handling sudden increases of generated data. If you are using a cloud-based service, you should be charged only for the virtual machines and the associated storage and resources your company consumes.
High performance: In real time, data is ingested from thousands of sources. A powerful streaming analytics tool should be able to process millions of events every second.
Security and compliance: Extended data usage within your company increases the urgency of following regulations and the importance of data security. To uphold strict security and compliance requirements, your team should have the ability to define and enforce universal regulation standards and be provided with a complete set of data security management tools from data encryption of all incoming and outgoing communications and in-memory processing to private networking.
Integration with external systems: Flexible integration options can help provide added value to your existing streamed data. For example, integrating with external data sources that live-stream data from events, or sites that provide real-time market information can help influence your own business choices.
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What is Streaming Analytics? Streaming analytics, also known as event stream processing (ESP), refers to the analyzing and processing of large volumes of data through the use of continuous queries. Traditionally, data is moved in batches. While batch processing may be an efficient method for handling huge pools of data, it is not suitable for time-sensitive, “in-motion” data that could otherwise be streamed, since that data can expire by the time it is processed. By using streaming...
Streaming analytics enables real-time logging, processing, and analyzing of data and events from streaming sources. This is essential in industries that consistently rely on real-time information, such as news corporations, financial companies, investment houses, and utility management companies.
Streaming analytics tools continuously run queries to gain information and perform actions on real-time data. When choosing a streaming analytics tool, here are some features to look out for:
Machine learning capabilities: Employ machine learning training models on real-time data streams to make continuous and accurate forecasts. Machine learning is also used to filter and enhance streamed data, and to help visualize collected data and generated predictions. In addition, you can utilize machine learning to help detect anomalies in your current data by comparing it with previously logged data.
SQL support: Continuous SQL queries perform powerful transformations on streaming data, saving data analysts a substantial amount of time and effort. SQL queries also enable users to detect events in real time by running queries from current streams against queries from past event streams.
User-friendly dashboards: Your streaming analytics tool should allow you to create operational dashboards with interactive charts and graphs that provide real-time monitoring and enable data transformation and alert configuration. You should also be able to create role-based user interfaces for different groups within your organization.
It is important that you have a graphical user interface of all data streams and data relationships with information on the streamed data’s origin, destination, and transformation history.
Support for multiple programming languages: Programmers and data analysts should be able to utilize code to automate the data streaming processes and execute various functions on the streamed data using any popular programming language, such as Python, Java, C#, Ruby, and JavaScript.
Rapid scaling: The ability to instantly scale your applications is essential for handling sudden increases of generated data. If you are using a cloud-based service, you should be charged only for the virtual machines and the associated storage and resources your company consumes.
High performance: In real time, data is ingested from thousands of sources. A powerful streaming analytics tool should be able to process millions of events every second.
Security and compliance: Extended data usage within your company increases the urgency of following regulations and the importance of data security. To uphold strict security and compliance requirements, your team should have the ability to define and enforce universal regulation standards and be provided with a complete set of data security management tools from data encryption of all incoming and outgoing communications and in-memory processing to private networking.
Integration with external systems: Flexible integration options can help provide added value to your existing streamed data. For example, integrating with external data sources that live-stream data from events, or sites that provide real-time market information can help influence your own business choices.
Hi @Anirban Bhattacharya, @reviewer1438992, @reviewer1582965, @Ertugrul Akbas, @Wallace Hugh and @reviewer1488372,
Please share your expert opinion.
Thanks.