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.
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.
Our organization was searching for a streaming analytics tool that would be able to adequately meet our needs. During our search, we compared many different streaming analytics tools and solutions. We found Databricks and Amazon Kinesis to be the most effective ones.
One of the things that I initially noticed about Azure Databricks was the high level of flexibility that it affords my team. Databricks is designed to enable us to customize both the way that our data processing solution functions and the platform where it runs. It makes it possible for us to ensure that we have the tools to conduct our data analysis in the way that best addresses our needs.
Databricks accomplishes this by providing us with a number of extremely useful features. These capabilities include:
Many common programming languages. Databricks is designed so that we can make use of the programming languages that are most appropriate for the project that we are undertaking.
Integration with Microsoft Azure. Databricks allows us to integrate our system with any Microsoft Azure solution that we want. We can tailor our system to best fit our analytical and data processing needs. Our team never needs to worry that some crucial feature is missing. The Microsoft Azure solution suite provides us with a wide variety of options. If something is missing, we can rely on the Azure suite to most likely have the function that we seek.
Cloud-native nature. Databricks is a cloud-native solution. It enables us to run all of our operations out of the cloud. Databricks is compatible with every major cloud provider. This means that we have the ability to use the cloud environment of our choice to host our data processing activities.
A major benefit that Databricks provides us is its flexibility. It enables us to handle workloads of many different sizes. Databricks’s cloud architecture has the ability to handle both large loads of data and much smaller tasks. This solution makes other data analysis and processing solutions unnecessary.
One of the aspects of Amazon Kinesis that I appreciate is the way that it enables us to take in, store, and process data in real time. Amazon Kinesis leverages a machine learning algorithm and provides us with the ability to quickly turn raw data into valuable insights. We do not need to devote hours or days to data processing. Amazon Kinesis’s ML capabilities provide us with immense value while at the same time saving us time and other resources.
We can also use this solution to scale up our data processing capabilities. Amazon Kinesis can be set to scale up our data stream processing as the need for growth increases. This function elastically expands our capabilities as the stream of data running through our system grows. Amazon Kinesis allows us to make sure that our system can keep up with our analysis and processing demands.
After we tried out both Databricks and Amazon Kinesis, we found that they both empowered and enabled us to take complete control of every aspect of our data analysis and processing process.
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