Top Tools / April 8, 2022
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Top 25 Stream Analytics Tools

Streaming analytics, often known as real-time analytics, is a sort of data analysis that displays real-time data and allows basic computations to be performed on it. In an organization that must deal with large sets of data each day, the appropriate tool will analyze and conduct other essential operations on the data regardless of scale. However, selecting a suitable stream analytics tool from the many available is complex.

Here is a top tools list of recommended stream analytics tools, together with their significant features, to make things easier and faster.


1. Memgraph

Memgraph is a graph streaming analytics tool that lets you explore data locally and in the cloud. This streaming analytics tool allows users such as big data developers and analysts to input data from various platforms and execute analysis without specialized solutions.

Key Features:

  • Python, Java, C#, PHP, Golang, Ruby, and JavaScript are examples of programming languages that can be used to connect.

  • Streaming analytics are provided by advanced AI and machine learning algorithms built into the platform, which can assist in making educated business decisions.

  • Memgraph provides a platform for developing user event tracking, permissions modeling, recommendation systems, and other applications.

Cost:

You can request a quote on their website.


2. Apache Spark

Apache Spark is a scalable streaming processing solution that natively handles batch and streaming workloads. Spark's unified batch and streaming programming models and one execution engine provide some distinct advantages over other traditional streaming solutions.

Key Features:

  • Fast recovery from failures and stragglers.

  • Improved load balancing and resource use.

  • Using streaming data in conjunction with static databases and interactive queries.

  • Integration with advanced processing libraries is native (SQL, machine learning, graph processing)

Cost:

You can request a quote on their website.


3. Apache Kafka

Apache Kafka is the most widely used and endorsed open-source distributed data streaming platform businesses use to handle real-time data streams. Kafka is most commonly used for microservice integration in the backend, and it can employ real-time data streaming channels like Spark or Flink. Data can also be transferred to other portals for numerical analysis using Kafka.

Key Features:

  • Most prevalent real-time data streaming tools.

  • Other real-time data streaming portals, such as Flink or Spark, can also be supported.

  • Store data streams safely in a distributed, durable, and fault-tolerant cluster.

  • There is a large community of open-source tools available.

Cost:

You can request a quote on their website.


4. Google Cloud DataFlow

Google Cloud DataFlow implements streaming analytics, allowing businesses to sift out ineffective data and slow down analyses. Companies may construct data pipelines using Apache Beam and Python to extract, transform, and analyze data from various IoT devices and other data sources.

For data streaming, Google recently removed Python 2 from Cloud DataFlow and replaced it with Python SDK and Python 3.

Key Features:

  • Apache Beam is used in conjunction with Python to define data pipelines that ensure the extraction, transformation, and analysis of data from various IoT devices and other data sources.

  • Google Cloud Platform (GCP) is a collection of cloud computing services used by Google for various businesses, including Gmail, Google Search, Google Docs, YouTube, and more.

  • It allows users to import data in various formats, including Parquet, CSV, Avro, and JSON.

Cost:

You can request a quote on their website.


5. Upsolver

Upsolver can acquire complex and streaming data through built-in connectors, define transformations using SQL commands, and continually output tables to your data lake or cloud data warehouse. With Upsolver, you can deliver analytics pipelines faster and increase data engineering productivity.

Key Features:

  • Easy-to-use and straightforward UI.

  • Professionalism and availability of the support team.

  • Smart, automated scaling capabilities are provided through pre-built scaling techniques.

  • All conventional and advanced SQL capabilities are available in Upsolver SQL.

Cost:

You can request a quote on their website.


6. Apache Samza

Apache Samza, LinkedIn’s framework for stream processing, is a scalable stream processing framework. It integrates with Apache Kafka and leverages Apache Hadoop YARN to offer fault tolerance, processor isolation, security, and resource management. Samza is in charge of snapshotting and restoring a stream processor's state.

Samza restores the processor's state to a consistent snapshot when restarted. Samza is designed to deal with massive volumes of state.

Key Features:

  • It enables you to process and evaluate data in real-time.

  • Use a simple API to express your application logic in a way that is independent of the data source.

  • Samza includes pre-configured connectors with Apache Kafka, AWS Kinesis, Azure EventHubs, ElasticSearch, and Apache Hadoop.

  • Flexible deployment options allow programs to run anywhere.

Cost:

You can request a quote on their website.


7. Lenses.io

Lenses.io delivers a user-friendly platform for developers to do operations. By allowing teams to monitor, investigate, protect, and deploy on their data platform, enterprises can turn their attention to data-driven business outcomes and let engineers reclaim their weekends.

Key Features:

  • Revealing insights about Kafka themes

  • SQL processors with lenses provide seamless data integration capabilities.

  • Data protection policies and role-based user interfaces

  • Responsive UI for Kafka overview.

Cost:

You can request a quote on their website.


8. Apache Storm

Apache Storm, built by Twitter, is an ideal tool for real-time data analytics. Apache Storm aims explicitly at the transformation of data streams. Storm systems can also guarantee data processing, with the capacity to replay data that was not processed the first time successfully.

Key Features:

  • ETL, online machine learning, and many other applications benefit from Apache Storm.

  • It's also possible to link it with Hadoop to further boost throughput.

  • It's free and open-source, one of the Apache projects.

  • It can handle large amounts of data at rapid speeds.

Cost:

You can request a quote on their website.


9. RapidMiner

RapidMiner is yet another cloud-based application that allows you to create an end-to-end streaming data analytics platform. It's an open-source program with several capabilities, including automation, which will enable it to loop and repeat actions and perform in-database operations.

Real-time scoring is also incorporated, allowing you to work with statistical approaches using third-party software. All models for preprocessing, clustering, prediction, and transformation have been operationalized.

Key Features:

  • If you want to dive deep into data analysis, you can use interactive charts and graphs with zooming, panning, and other interactive capabilities.

  • Can analyze more than 40 different types of structured and unstructured data, including audio, video, pictures, social media, text, and NoSQL.

  • Provides predictive analytics and machine learning models for essential insights into corporate intelligence operations.

Cost:

You can request a quote on their website.


10. Talend

Talend is a stream analytics platform that simplifies challenging streaming technologies and data integration projects with Kafka and Kinesis. It's designed for modern data formats, including AVRO, JSON, Parquet, and CSV, and it works with Salesforce, AWS S3, Google Cloud Storage, and various cloud databases.

Key Features:

  • Talend is an entirely free application to use.

  • Accelerates, simplifies, and expands access to streaming data integration.

  • The ease with which you can design your data pipelines and ETL process for your business data is great and deliver reports on time

Cost:

You can request a quote on their website.


11. IBM Streaming Analytics

IBM Streaming Analytics is a sophisticated analytics platform that intends to assist enterprises in managing their user-created applications to analyze better, ingest, and compare data from various sources. It has an Eclipse-based IDE (Integrated Development Environment) and support for the programming languages Java, Python, and Scala. As a result, developing real-time data analytics apps becomes easier.

Key Features:

  • Enables you to create notebooks for Python users to monitor, manage, and make educated decisions easily.

  • Data engineers can connect to any data source, organized or unstructured, and integrate it with Hadoop, Spark, and other data infrastructures.

  • In real-time, data may be ingested and correlated from thousands of sources.

Cost:

You can request a quote on their website.


12. Oracle stream analytics

Oracle stream analytics is a sophisticated solution that assists businesses in obtaining the appropriate information or data from a variety of sources. Its data streaming feature delivers real-time data insights critical for making business decisions that affect current and future lead connections.

Key Features:

  • Enables users to find interesting events in real-time by running queries against event streams.

  • Permits users to create bespoke operational dashboards that provide real-time monitoring, data transformation, and alerting based on stream analysis.

  • Graphical representations of tabular streaming data are provided.

Cost:

You can request a quote on their website.


13. FICO data management solutions

FICO data management solutions seek to assist enterprises in locating relevant data to make important business choices. With so much data available to most businesses, knowing how to use it effectively and acquire the most relevant data is critical to being productive.

This platform gives significant insights collected from various sources to help you make the best decisions possible. Data is collected from both structured and unstructured sources by FICO's Data Management Solutions.

Key Features:

  • From a wide range of sources, quickly develop significant insights and precise decision-making.

  • It's possible to use it on-premises or in the cloud.

  • It enables you to consume and correlate data from structured and unstructured sources.

Cost:

You can request a quote on their website.


14. SQLStream

SQLStream is another SQL extension that allows for the processing of streaming data. StreamSQL makes creating programs that enable data stream deception, real-time conformance, monitoring, and network security easier. The simplicity of StreamSQL for real-time information computation in massive data is critical to its effectiveness. Because of its simplicity, StreamSQL is also suitable for non-developers.

Key Features:

  • Ensure data stream manipulation, real-time compliance, surveillance, and monitoring networks

  • Delivers increased speed, ease of use, and faster analytics responsiveness.

  • Offers services such as Threat Detection, Anomaly Detection, Predictive Analytics, and Sentiment Analysis.

Cost:

You can request a quote on their website.


15. Confluent

Confluent is a dependable and productive stream analytics platform that delivers a steady stream of real-time event streams and real-time stream processing to power the data-driven back-end operations and rich front-end experiences required for any business to thrive in today's competitive, consumer-driven market markets.

Key Features:

  • Allows managing significant amounts of data with more flexibility for the processing needs.

  • Streaming technology that can be tweaked and scaled.

  • The ability to scale up to any level based on the organization's accessible commodity gear.

Cost:

You can request a quote on their website.


16. Kibana

Kibana is an analytics and visualization software with a wide range of options. It gives a real-time summary and display of streaming data to businesses. Kibana's user-friendly interface allows users to share and embed dashboards in seconds.

Key Features:

  • There are numerous ways to visualize and analyze your data.

  • It also has a monitoring and management interface for our Elastic cluster.

  • Can filter logs based on a variety of factors.

Cost:

You can request a quote on their website.


17. Ably Realtime

Ably Realtime is a real-time pub/sub messaging platform for enterprises. The platform works with back-end streaming and stream processing solutions, allowing enterprises to create, launch rapidly, and scale key real-time functionality supplied to end consumers.

Key Features:

  • Payments and fraud detection in real-time.

  • IoT sensor data

  • Dashboards that are updated in real-time

  • Monitoring of logs, traffic, and networks

  • User behavior tracking and context-aware internet advertising

  • Vehicle tracking and geofencing

Cost:

You can request a quote on their website.


18. WSO2 complex event processor

WSO2 complex event processor is an open-source program that enables businesses to detect useful information or events in real-time across numerous data sources for their benefit. WSO2 manages data precisely and reacts quickly to actual business situations that may be useful or dangerous. These data can be examined for their potential impact, and the results can be shared with the public.

Key Features:

  • Thanks to customizable dashboards with interactive and integrated widgets, users get at-a-glance views and drill-down capabilities.

  • Allows you to create your analytics visualizations using widgets.

  • Different visualization perspectives based on user roles and access control have been mastered.

Cost:

You can request a quote on their website.


19. TIBCO Spotfire

TIBCO Spotfire is a cloud-ready streaming analytics platform that allows developers to swiftly create real-time applications at a fraction of the cost and risk of alternatives. It uses a solid Eclipse-based integrated development environment and graphical language to assist developers in delivering apps and incremental releases.

Key Features:

  • A multi-threaded event server optimized for real-time streaming event data processing contains tools for developing features for increased speed, scalability, and failover.

  • Over 150 pre-built adapters and visualization choices link to real-time and historical data sources, allowing you to easily and quickly increase data quantities for complete input without requiring bespoke development or integration.

  • TIBCO StreamBase EventFlow, a visual programming language designed exclusively for real-time event processing, accelerates development and iterations.

Cost:

You can request a quote on their website.


20. Apache Flink

Apache Flink is a renowned stream analytics platform designed to run in various cluster setups and execute calculations on data in a short amount of time, regardless of scale. This platform can handle both unbounded and bounded data sets with ease.

Key Features:

  • Streaming data and event-based capabilities can provide real-time insights.

  • Flink allows for real-time data analytics on streaming data and is well-suited to continuous Extract-Transform-Load (ETL) pipelines on streaming data and event-driven applications.

  • Flink provides a fault recovery strategy based on periodic and asynchronous checkpointing.

Cost:

You can request a quote on their website.


21. Circonus

Circonus is a monitoring and analytics software designed for today's business. Circonus provides crystal-clear, real-time visibility into the behavior, health, trends, and performance of traditional infrastructure and cloud-based technologies in one powerful, unified platform.

Key Features:

  • Circonus is a unified system that combines numerous monitoring, alerting, event processing, and analytics tools.

  • In the ingestion pipeline, incoming data is examined, allowing for actual real-time notifications.

  • Machine learning capabilities provide customers with predictive, highly accurate insights that offer them a strategic advantage in growing their businesses.

Cost:

You can request a quote on their website.


22. Amazon Kinesis

Amazon Kinesis Data Analytics is an AWS service that specializes in streaming data analytics. You can take streaming data, analyze it, and acquire the insights you need in near real-time, including applying machine learning queries, using this tool designed for quick ETL.

You can also use SQL to build up your analysis for quick results. It is designed to function in all typical cluster configuration situations, scale quickly, and execute at in-memory speeds.

Key Features:

  • Obtain real-time insights from streaming data using machine learning.

  • Kinesis Data Analytics elastically scales your application to accommodate the data throughput of your source stream and the complexity of your queries.

  • The Amazon Kinesis Data Analytics service is available on a pay-as-you-go basis.

Cost:

You can request a quote on their website.


23. Informatica

Informatica is a stream analytics platform that uses artificial intelligence to enable data engineers to ingest, process, and analyze real-time streaming data for actionable insights. This platform optimizes the collection of the structured and unstructured stream and machine data warehousing appliances, such as Hadoop and other analytics platforms.

Key Features:

  • Real-time data ingestion and integration.

  • Spark structured streaming is a technology that allows you to stream data in a structured way.

  • Enhanced connectivity.

  • Advanced data transformation in real-time

  • Support for cloud-ready streams.

Cost:

You can request a quote on their website.


24. Striim

Striim is a real-time data integration and intelligence platform that can stream data in real-time from a range of sources. Striim is capable of facilitating intelligent decisions by analyzing all data. It also focuses on making real-time hybrid cloud integrations easier and simplifying the management of streaming data applications.

Key Features:

  • A single package includes real-time data ingestion, stream processing, pipeline monitoring, and real-time delivery with validation.

  • It continually ingests a wide variety of high-volume, high-velocity data from enterprise databases using low-impact change data capture (CDC), Hadoop, cloud apps, and IoT devices.

  • Built-in dashboards and monitoring allow you to view the status of your data flows in real-time and discover bottlenecks quickly.

Cost:

You can request a quote on their website.


25. Azure Stream Analytics

Azure Stream Analytics utilizes SQL to build and deploy low-cost solutions for gaining real-time insights from connected devices, infrastructures, and apps. Firms may stream millions of events in seconds with this stream analytics software.

Key Features:

  • Data from different streams can be merged.

  • For data transformations, declarative SQL-based queries can be used.

  • With Power BI, you can stream data to real-time dashboards.

  • You can use Azure IoT Hub to connect your devices.

Cost:

You can request a quote on their website.


Things to Consider When Choosing a Stream Analytics Tool

Developer Productivity

The easy SQL-based query language with powerful temporal limitations for analyzing data in motion is one of the most significant features of Stream Analytics. To write powerful queries, you only need to be familiar with the SQL language. A decent stream analytics platform must offer language expansion via JavaScript user-defined functions (UDFs) or user-defined aggregates to execute sophisticated calculations as part of a Stream Analytics query.

Data Storage Options

Another primary requirement is deciding between a SaaS solution and on-premises hosting. SaaS solutions are cloud-based and managed by a third-party supplier who assumes responsibility for a platform's IT infrastructure, including storage, security, and data backup.

SaaS hosting is a scalable, cost-effective solution that may shorten time-to-market and interface with other systems. As a result, make sure that the storage options you select align with your company's goals.

Professional Services

Check to see if the provider's services are necessary for you. Some solutions, particularly SaaS offerings, are founded on business models that profit from their usefulness. Consider whether you'd like to work with a partner who can provide you with hands-on help and advice on how to get the most out of their solution.


Conclusion

We hope you have a clear grasp of what stream analytics tools are and how to use them. It is up to you to select the right tools that coincide with your firm's aims and expectations while remaining within your budget.


FAQs

What is Stream Analytics?

Streaming analytics, often known as real-time analytics, is a sort of data analysis that displays real-time data and allows basic computations to be performed on it. It employs a technique known as stream processing, which involves processing massive amounts of continually changing data. Compared to working with historical data, working with real-time data necessitates slightly different procedures.

This sort of analytics focuses on data flows rather than more difficult analytical activities. Its primary goal is to provide users with up-to-date information and keep data in a current state.

What Are Stream Analytics Tools?

Stream Analytics Tools uses continuous queries to conduct actions on real-time data instead of batch-wise data processing, which can result in out-of-date data. Analytics companies may use data in motion to reduce missed opportunities and create new ones with streaming.

When Should You Consider Using Stream Analytics Tools?

Streaming analytics is the continual processing and analysis of data records. Stream Analytics Tools should be implemented from the outset in companies that deal with large amounts of data on a daily basis in order to assess faults and make timely decisions.

What Are the Benefits of Stream Analytics Tools?

The benefits of an organization using Stream Analytics tools are:

  • Data visualization.

  • Business insights.

  • Increased competitiveness.

  • Reducing avoidable losses.

  • Analyzing routine business operations.

What Is ETL?

Extract, Transform, and Load (ETL) is a data integration process that combines data from several sources into a single, consistent data store, which is then put into a data warehouse or other destination system.

Businesses extensively use ETL to:

  • Extract data from legacy systems.

  • Clean the data to enhance its quality and consistency.

  • Load data into the desired database.

Top 25 Stream Analytics Tools
StartupStash

The world's biggest online directory of resources and tools for startups and the most upvoted product on ProductHunt History.