# Best Enterprise Event Stream Processing Software *By [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)* Products classified in the overall Event Stream Processing category are similar in many regards and help companies of all sizes solve their business problems. However, enterprise business features, pricing, setup, and installation differ from businesses of other sizes, which is why we match buyers to the right Enterprise Business Event Stream Processing to fit their needs. Compare product ratings based on reviews from enterprise users or connect with one of G2's buying advisors to find the right solutions within the Enterprise Business Event Stream Processing category. In addition to qualifying for inclusion in the Event Stream Processing Software category, to qualify for inclusion in the Enterprise Business Event Stream Processing Software category, a product must have at least 10 reviews left by a reviewer from an enterprise business. ## Category Overview **Total Products under this Category:** 70 ## Trust & Credibility Stats **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 2,300+ Authentic Reviews - 70+ Products - Unbiased Rankings G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category. ## Best Event Stream Processing Software At A Glance - **Best for Small Businesses:** [Aiven for Apache Kafka](https://www.g2.com/products/aiven-for-apache-kafka/reviews) - **Best for Mid-Market:** [Aiven for Apache Kafka](https://www.g2.com/products/aiven-for-apache-kafka/reviews) - **Best for Enterprise:** [IBM StreamSets](https://www.g2.com/products/ibm-streamsets/reviews) - **Highest User Satisfaction:** [Aiven for Apache Kafka](https://www.g2.com/products/aiven-for-apache-kafka/reviews) - **Best Free Software:** [Aiven for Apache Kafka](https://www.g2.com/products/aiven-for-apache-kafka/reviews) --- **Sponsored** ### Kpow for Apache Kafka® Kpow is a sophisticated enterprise Kafka management tool designed to enhance the experience of engineering teams by providing a comprehensive solution for managing, monitoring, exploring, and securing Kafka environments. This JVM-based web application serves as an all-in-one console, empowering Kafka engineers with the capabilities they need to streamline their operations and improve productivity. Targeted primarily at engineering teams working with Kafka, Kpow addresses the complexities of managing multiple Kafka clusters, schema registries, and connection installations. With Kpow, users can efficiently monitor and control their Kafka resources from a single interface, simplifying the management process and reducing the time spent on routine tasks. The tool is particularly beneficial for organizations that rely heavily on Kafka for data streaming and processing, as it provides essential functionalities that enhance observability and operational efficiency. One of the standout features of Kpow is its real-time monitoring and visualization capabilities. Users can quickly identify unbalanced brokers and gain insights into how data is distributed across their Kafka Streams topologies. This level of visibility is crucial for diagnosing production issues and optimizing performance. Kpow's advanced search functionalities, including Data Inspect, Streaming Search, and kREPL, enable users to search through vast amounts of messages at remarkable speeds, allowing for rapid troubleshooting and data analysis. Kpow also prioritizes security and access control, making it suitable for enterprise environments. It integrates seamlessly with standard authentication providers and offers role-based access controls, ensuring that user actions can be finely tuned to meet organizational security requirements. Additional security features, such as data masking and audit logs, further enhance the tool's capability to operate in sensitive environments, including air-gapped installations. Installation of Kpow is straightforward, requiring only a single Docker container or JAR file, which operates efficiently with minimal resource requirements of 1GB memory and 1 CPU for production use. This ease of deployment, combined with its powerful features, positions Kpow as a valuable asset for organizations looking to maximize their Kafka infrastructure while maintaining robust security and operational control. [Visit company website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=1509&secure%5Bdisplayable_resource_id%5D=1509&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=1509&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=133071&secure%5Bresource_id%5D=1509&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fevent-stream-processing%2Fmid-market&secure%5Btoken%5D=57299f10ef340d17f676f6d9fd9ff39310222b3b3f3a8f2b80938a28f193c587&secure%5Burl%5D=http%3A%2F%2Ffactorhouse.io%2F&secure%5Burl_type%5D=custom_url) --- ## Top-Rated Products (Ranked by G2 Score) ### 1. [IBM StreamSets](https://www.g2.com/products/ibm-streamsets/reviews) IBM StreamSets is a robust streaming data integration tool for hybrid, multi-cloud environments that enables real-time decision making. It allows ingestion and in-flight transformation of structured, unstructured, and semi-structured data from streaming sources, and reliably delivers trusted data into diverse destinations. Flexible deployment options promote security, cost-effectiveness and performance. With several pre-built connectors, an intuitive no-code/low-code interface, and automatic adaptability to data drifts, StreamSets accelerates data pipeline operationalization. It integrates with IBM’s broader data integration capabilities, enabling reliable pipelines that unify multiple data integration patterns, underpinned by data observability capabilities for continuous data quality monitoring and remediation. That’s why the largest companies in the world trust StreamSets to power millions of data pipelines for modern analytics, data science, smart applications, and hybrid integration. **Average Rating:** 4.0/5.0 **Total Reviews:** 115 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.2/10 (Category avg: 8.9/10) - **Data Sources:** 8.0/10 (Category avg: 8.6/10) - **Data Processing:** 8.2/10 (Category avg: 8.8/10) - **Real-Time Processing:** 9.0/10 (Category avg: 9.1/10) **Seller Details:** - **Seller:** [IBM](https://www.g2.com/sellers/ibm) - **Year Founded:** 1911 - **HQ Location:** Armonk, NY - **Twitter:** @IBM (709,023 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®) - **Ownership:** SWX:IBM **Reviewer Demographics:** - **Who Uses This:** Data Engineer, Software Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 42% Enterprise, 33% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (30 reviews) - User Interface (16 reviews) - Data Management (15 reviews) - Data Pipelining (15 reviews) - Integrations (14 reviews) **Cons:** - Learning Curve (13 reviews) - Expensive (10 reviews) - Learning Difficulty (8 reviews) - Slow Performance (8 reviews) - Steep Learning Curve (8 reviews) ### 2. [Aiven for Apache Kafka](https://www.g2.com/products/aiven-for-apache-kafka/reviews) Aiven for Apache Kafka® is a fully managed distributed event streaming service, that can be deployed in the cloud of your choice. Aiven for Apache Kafka is ideal for event-driven applications, near-real-time data transfer and data pipelines, streaming analytics, and any use case that requires moving huge amounts of real-time data between applications and systems. With Aiven for Apache Kafka you can set up fully managed Kafka clusters in less than 10 minutes — using the Aiven web console or programmatically via Aiven’s API, CLI, Terraform provider or Kubernetes operator. You can easily connect it to your existing tech stack with a fully managed Apache Kafka Connect service with over 30+ connectors. Monitoring your clusters with logs and metrics is also available out of the box via multiple service integrations. Get access to a complete open source ecosystem of streaming technologies and tools around Apache Kafka to fully manage, and operate a real time data infrastructure at scale using: Aiven for Apache Kafka: the core event streaming framework allowing you to transport data within your organization Aiven for Apache Kafka Connect: a fully managed, fully open source, distributed service enabling you to integrate your existing data sources and sinks seamlessly with Aiven for Apache Kafka. Aiven for Apache Kafka MirrorMaker2: a fully managed, fully open source distributed data replication service for cluster to cluster data replication, disaster recovery and geo proximity across multiple regions. Karapace®: a fully open source Kafka Schema Registry that applications can access to serialize and deserialize messages with popular formats such as AVRO, Protobuf and JSON. Aiven for Apache Flink®: a fully managed, fully open source streaming SQL engine for stateful stream processing over your data streams. Klaw: an open source data governance tool that helps enterprises exercise Apache Kafka® topic and schema governance. Aiven is ISO / IEC 27001: 2013, SOC 2, HIPAA, GDPR, and CCPA compliant. Check our pricing and try our free 30-day trial at https://aiven.io/kafka. **Average Rating:** 4.3/5.0 **Total Reviews:** 244 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 8.9/10) - **Data Sources:** 8.4/10 (Category avg: 8.6/10) - **Data Processing:** 8.4/10 (Category avg: 8.8/10) - **Real-Time Processing:** 8.5/10 (Category avg: 9.1/10) **Seller Details:** - **Seller:** [Aiven](https://www.g2.com/sellers/aiven) - **Company Website:** https://aiven.io/ - **Year Founded:** 2016 - **HQ Location:** Helsinki, Southern Finland - **Twitter:** @aiven_io (4,084 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/10294984/ (439 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Software Engineer, Senior Software Engineer - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 44% Mid-Market, 29% Small-Business #### Pros & Cons **Pros:** - Setup Ease (29 reviews) - Ease of Use (24 reviews) - Scaling (17 reviews) - Management Ease (14 reviews) - Reliability (14 reviews) **Cons:** - Expensive (28 reviews) - Poor Documentation (8 reviews) - Limited Features (7 reviews) - Complexity (6 reviews) - Not User-Friendly (5 reviews) ### 3. [Apache Kafka](https://www.g2.com/products/apache-kafka/reviews) Apache Kafka is an open-source distributed event streaming platform developed by the Apache Software Foundation. It is designed to handle real-time data feeds with high throughput and low latency, making it ideal for building data pipelines, streaming analytics, and integrating data across various systems. Kafka enables organizations to publish, store, and process streams of records in a fault-tolerant and scalable manner, supporting mission-critical applications across diverse industries. Key Features and Functionality: - High Throughput and Low Latency: Kafka delivers messages at network-limited throughput with latencies as low as 2 milliseconds, ensuring efficient data processing. - Scalability: It can scale production clusters up to thousands of brokers, handling trillions of messages per day and petabytes of data, while elastically expanding and contracting storage and processing capabilities. - Durable Storage: Kafka stores streams of data safely in a distributed, durable, and fault-tolerant cluster, ensuring data integrity and availability. - High Availability: The platform supports efficient stretching of clusters over availability zones and connects separate clusters across geographic regions, enhancing resilience. - Stream Processing: Kafka provides built-in stream processing capabilities through the Kafka Streams API, allowing for operations like joins, aggregations, filters, and transformations with event-time processing and exactly-once semantics. - Connectivity: With Kafka Connect, it integrates seamlessly with hundreds of event sources and sinks, including databases, messaging systems, and cloud storage services. Primary Value and Solutions Provided: Apache Kafka addresses the challenges of managing real-time data streams by offering a unified platform that combines messaging, storage, and stream processing. It enables organizations to: - Build Real-Time Data Pipelines: Facilitate the continuous flow of data between systems, ensuring timely and reliable data delivery. - Implement Streaming Analytics: Analyze and process data streams in real-time, allowing for immediate insights and actions. - Ensure Data Integration: Seamlessly connect various data sources and sinks, promoting a cohesive data ecosystem. - Support Mission-Critical Applications: Provide a robust and fault-tolerant infrastructure capable of handling high-volume and high-velocity data, essential for critical business operations. By leveraging Kafka's capabilities, organizations can modernize their data architectures, enhance operational efficiency, and drive innovation through real-time data processing and analytics. **Average Rating:** 4.5/5.0 **Total Reviews:** 126 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 8.9/10) - **Data Sources:** 8.7/10 (Category avg: 8.6/10) - **Data Processing:** 9.0/10 (Category avg: 8.8/10) - **Real-Time Processing:** 9.1/10 (Category avg: 9.1/10) **Seller Details:** - **Seller:** [The Apache Software Foundation](https://www.g2.com/sellers/the-apache-software-foundation) - **Year Founded:** 1999 - **HQ Location:** Wakefield, MA - **Twitter:** @TheASF (66,116 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/215982/ (2,408 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Software Engineer, Senior Software Engineer - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 47% Enterprise, 33% Mid-Market #### Pros & Cons **Pros:** - Scalability (5 reviews) - Real-time Data (3 reviews) - Easy Integrations (2 reviews) - Performance (2 reviews) - Performance Efficiency (2 reviews) **Cons:** - Complexity (1 reviews) - Data Management Issues (1 reviews) - Debugging Issues (1 reviews) - Difficult Learning (1 reviews) - Limited Customization (1 reviews) ### 4. [Confluent](https://www.g2.com/products/confluent/reviews) Cloud-native service for data in motion built by the original creators of Apache Kafka® Today’s consumers have the world at their fingertips and hold an unforgiving expectation for end-to-end real-time brand experiences. Data in motion is the underlying, fundamental ingredient to any truly connected customer experience. It provides a continuous supply of real- time event streams coupled with real-time stream processing to power the data-driven backend operations and rich front-end experiences necessary for any business to succeed within today’s competitive, consumer-driven markets. Set your data in motion while avoiding the headaches of infrastructure management and focus on what matters most: your business. Built by the original creators of Apache Kafka, Confluent Cloud is a fully managed, cloud-native service for connecting and processing all of your real-time data, everywhere it’s needed. **Average Rating:** 4.4/5.0 **Total Reviews:** 110 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.5/10 (Category avg: 8.9/10) - **Data Sources:** 8.8/10 (Category avg: 8.6/10) - **Data Processing:** 8.8/10 (Category avg: 8.8/10) - **Real-Time Processing:** 9.0/10 (Category avg: 9.1/10) **Seller Details:** - **Seller:** [Confluent](https://www.g2.com/sellers/confluent) - **Year Founded:** 2014 - **HQ Location:** Mountain View, California - **Twitter:** @ConfluentInc (43,597 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/88873/ (3,706 employees on LinkedIn®) - **Ownership:** NASDAQ: CFLT **Reviewer Demographics:** - **Who Uses This:** Software Engineer, Senior Software Engineer - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 36% Enterprise, 35% Small-Business #### Pros & Cons **Pros:** - Cloud Computing (1 reviews) - Cloud Services (1 reviews) - Connectors (1 reviews) - Data Integration (1 reviews) - Ease of Use (1 reviews) **Cons:** - Cost Estimation (1 reviews) - Expensive (1 reviews) - Initial Difficulties (1 reviews) - Lack of Features (1 reviews) - Learning Curve (1 reviews) ### 5. [Amazon Kinesis Data Streams](https://www.g2.com/products/aws-amazon-kinesis-data-streams/reviews) Amazon Kinesis Data Streams is a massively scalable, durable, and low-cost streaming data service. Kinesis Data Streams can continuously capture gigabytes of data per second from hundreds of thousands of sources, such as website clickstreams, database event streams, financial transactions, social media feeds, IT logs, and location-tracking events. The collected data is available in milliseconds to allow real-time analytics use cases, such as real-time dashboards, real-time anomaly detection, dynamic pricing. Customers run more than two million unique streams and process tens of PB of data per day with Amazon Kinesis Data Streams. **Average Rating:** 4.3/5.0 **Total Reviews:** 81 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 8.9/10) - **Data Sources:** 9.1/10 (Category avg: 8.6/10) - **Data Processing:** 9.0/10 (Category avg: 8.8/10) - **Real-Time Processing:** 9.4/10 (Category avg: 9.1/10) **Seller Details:** - **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd) - **Year Founded:** 2006 - **HQ Location:** Seattle, WA - **Twitter:** @awscloud (2,223,984 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (156,424 employees on LinkedIn®) - **Ownership:** NASDAQ: AMZN **Reviewer Demographics:** - **Who Uses This:** Software Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 37% Mid-Market, 37% Enterprise #### Pros & Cons **Pros:** - Real-time Data (2 reviews) - Real-Time Processing (2 reviews) - Real-time Streaming (2 reviews) - Streaming (2 reviews) - API Integration (1 reviews) **Cons:** - Difficult Setup (2 reviews) - Expensive (2 reviews) - Resource Intensive Learning (2 reviews) - Complexity (1 reviews) - Complexity Issues (1 reviews) ### 6. [Red Hat OpenShift Streams for Apache Kafka](https://www.g2.com/products/red-hat-openshift-streams-for-apache-kafka/reviews) Red Hat® OpenShift® Streams for Apache Kafka is a managed cloud service that provides a streamlined developer experience for building, deploying, and scaling new cloud-native applications or modernizing existing systems. **Average Rating:** 4.4/5.0 **Total Reviews:** 26 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 9.2/10 (Category avg: 8.9/10) - **Data Sources:** 8.6/10 (Category avg: 8.6/10) - **Data Processing:** 8.3/10 (Category avg: 8.8/10) - **Real-Time Processing:** 8.5/10 (Category avg: 9.1/10) **Seller Details:** - **Seller:** [Red Hat](https://www.g2.com/sellers/red-hat) - **Year Founded:** 1993 - **HQ Location:** Raleigh, NC - **Twitter:** @RedHat (299,757 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/3545/ (19,305 employees on LinkedIn®) **Reviewer Demographics:** - **Top Industries:** Computer Software - **Company Size:** 42% Small-Business, 38% Enterprise ### 7. [Spark Streaming](https://www.g2.com/products/spark-streaming/reviews) Spark Streaming brings Apache Spark's language-integrated API to stream processing, letting you write streaming jobs the same way you write batch jobs. It supports Java, Scala and Python. Spark Streaming recovers both lost work and operator state (e.g. sliding windows) out of the box, without any extra code on your part. **Average Rating:** 4.2/5.0 **Total Reviews:** 38 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.5/10 (Category avg: 8.9/10) - **Data Sources:** 9.0/10 (Category avg: 8.6/10) - **Data Processing:** 9.2/10 (Category avg: 8.8/10) - **Real-Time Processing:** 9.0/10 (Category avg: 9.1/10) **Seller Details:** - **Seller:** [The Apache Software Foundation](https://www.g2.com/sellers/the-apache-software-foundation) - **Year Founded:** 1999 - **HQ Location:** Wakefield, MA - **Twitter:** @TheASF (66,116 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/215982/ (2,408 employees on LinkedIn®) **Reviewer Demographics:** - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 43% Mid-Market, 30% Small-Business ## Parent Category [Big Data Software](https://www.g2.com/categories/big-data) ## Related Categories - [Big Data Processing And Distribution Systems](https://www.g2.com/categories/big-data-processing-and-distribution) - [ETL Tools](https://www.g2.com/categories/etl-tools) - [Stream Analytics Software](https://www.g2.com/categories/stream-analytics) --- ## Buyer Guide ### What You Should Know About Event Stream Processing Software ### What is Event Stream Processing Software? Data is stored and subsequently processed with traditional data processing tools. This method is not effective when data is constantly changing, as by the time the data has been stored and analyzed, it has likely already changed and become obsolete. Event stream processing, also known as stream processing, helps ease these concerns by processing the data when it is on the move. As opposed to batch processing, which focuses on data at rest, stream processing allows for the processing of an uninterrupted flow of records. With event stream processing, the data is constantly arriving, with the focus being on identifying how the data has changed over time or detecting anomalies in the historical data, or both. Key Benefits of Event Stream Processing Software - Allow for extremely low latency - Analyze data in real time - Scale data processing, giving the user the ability to handle any amount of streaming data and process data from numerous sources ### Why Use Event Stream Processing Software? Event stream processing software is incomplete without the ability to manipulate data as it arrives. This software assists with on-the-fly processing, letting users aggregate, perform joins of data within a stream, and more. Users leverage stream processing tools to process data transferred among a whole range of internet of things (IoT) endpoints and devices, including smart cars, machinery, or home appliances. Real-time data processing is key when companies want deeper insight into their data; it is also helpful when time is of the essence—for example, in the case of retail companies looking to keep a constant and consistent record of their inventory across multiple channels. **Gain insights from data —** Users leverage event stream processing software as a buffer to connect a company’s many data sources to a data storage solution, such as a data lake. From movie watching on a streaming service to taxi rides on a ride-hailing app, this data can be used for pattern identification and to inform business decisions. **Real time integration—** Through the continuous collection of data from data sources, such as databases, sensors, messaging systems, and logs, users are able to ensure their applications which rely on this data are up to date. **Control data flows —** Event stream processing software makes it easier to create, visualize, monitor, and maintain data flows. ### Who Uses Event Stream Processing Software? Business users working with data use event stream processing software which gives them access to data in real time. **Developers —** Developers looking to build event streaming applications that rely on the flow of big data benefit from event stream processing software. For example, batch processing does not serve an application well that is aimed at providing recommendations based on real-time data. Therefore, developers rely on event stream processing software to best handle this data and process it effectively and efficiently. **Analysts —** To analyze big data as it comes, analysts need to utilize a tool that processes the data. With event stream processing software, they are equipped with the proper tools to integrate the data into their analytics platforms. **Machine learning engineers —** Data is a key component of the training and development of machine learning models. Having the right data processing software in place is an important part of this process. ### Kinds of Event Stream Processing software There are different methods or manners in which the stream processing takes place. **At-rest analytics —** Like log analysis, at rest-analytics looks back on historical data to find trends. **In-stream analytics —** A more complex form of analysis occurs with in-stream analytics in which data streams between or across devices are analyzed. **Edge analytics —** This method has the added benefit of potentially lowering the latency for data that is processed on device (for example an IoT device), as the data does not necessarily need to be sent to the cloud. ### Event Stream Processing Software Features Event stream processing software, with processing at its core, provides users with the capabilities they need to integrate their data for purposes such as analytics and application development. The following features help to facilitate these tasks: **Connectors —** With connectors to a wide range of core systems (e.g., via an API), users extend the reach of existing enterprise assets. **Metrics —** Metrics help users analyze the processing to ascertain its performance. **Change data capture (CDC) —** CDC turns databases into a streaming data source where each new transaction is delivered to event stream processing software instantaneously. **Data validation—** Data validation allows users to visualize the data flow and ensure their data and data delivery is validated. **Pre-built data pipelines —** Some tools provide pre-built data pipelines to enable operational workloads in the cloud. ### Trends Related to Event Stream Processing Software Although data has been around in some form for a long while, the sheer volume, velocity, and variety due to innovations like IoT is unprecedented. As such, technology like artificial intelligence (AI) is helping to make data management and processing manageable. **Internet of things (IoT) —** With the proliferation of IoT comes the proliferation of varied data types. Event stream processing software must facilitate the processing of these multifarious data types. Also, IoT data is typically fast moving and frequently changing. It is critical that these solutions provide the ability to ingest and integrate this kind of data. **Embedded AI —** Machine and deep learning functionality is getting increasingly embedded in nearly all types of software, irrespective of whether the user is aware of it or not. The use of embedded AI inside software like CRM, marketing automation, and analytics solutions is allowing users to streamline processes, automate certain tasks, and gain a competitive edge with predictive capabilities. Data integration tools like event stream processing software will become increasingly more important, as AI is fueled by data. Embedded AI may gradually pick up and it may do so in the way cloud deployment and mobile capabilities have over the past decade or so. Eventually, vendors may not need to highlight their product benefits from machine learning as it may just be assumed and expected. **Self service offerings —** As with other types of data tools (such as analytics platforms), there is an increasing trend for software to be of the self-service nature. This means that nonprofessionals should be able to use the tool easily with little to no IT support for setting it up. With drag-and-drop interfaces or highly customizable setups, average business users are being empowered by statistical analysis capabilities. ### Potential Issues with Event Stream Processing Software **Data organization —** It may be challenging to organize data in a way that is easily accessible and harness big data sets that contain historical and real-time data. Companies often need to build a data warehouse or a data lake that combines all the disparate data sources for easy access. This requires highly skilled employees. **Deployment issues —** Search software requires lots of work by a skilled development team or vendor support staff to properly deploy the solution, especially if the data is particularly messy. Some data may lack compatibility with different products while some solutions may be geared for different types of data. For example, some solutions may not be optimized for unstructured data, whilst others may be the best fit for numerical data. ### Software and Services Related to Event Stream Processing Software The following solutions can be used in conjunction with or instead of the products in this category to be able to integrate and analyze data. **Stream analytics software —** [Stream analytics software](https://www.g2.com/categories/stream-analytics) helps users looking for tools specifically geared toward analyzing, as opposed to just processing data in real time. These tools help users analyze data in transfer through APIs, between applications, and more. This software is helpful with IoT data that needs frequent analysis in real time. **Big data integration platforms —** [Big data integration platforms](https://www.g2.com/categories/big-data-integration-platform) are robust and help users manage and store big data clusters and use them within cloud applications. **Analytics platforms —** [Analytics platforms](https://www.g2.com/categories/analytics-platforms) include big data integrations, but are broader-focused tools which facilitate five elements: data preparation, data modeling, data blending, data visualization, and insights delivery. **Log analysis software —** [Log analysis software](https://www.g2.com/categories/log-analysis) is a tool that gives users the ability to analyze log files. This type of software includes visualizations and is particularly useful for monitoring and alerting purposes. **Data preparation software —** Key solutions necessary for easy data analysis is [data preparation software](https://www.g2.com/categories/data-preparation) and other related data management tools. These solutions allow users to discover, combine, clean, and enrich data for simple analysis. Data preparation tools are used by IT or data analysts tasked with using business intelligence tools. Some business intelligence platforms offer data preparation features but businesses with a wide range of data sources often opt for a dedicated data preparation tool. **Data warehouses—** Most companies have a large number of disparate data sources. To best integrate all their data, they implement [data warehouse software](https://www.g2.com/categories/data-warehouse). Data warehouses house data from multiple databases and business applications, that allow business intelligence and analytics tools to pull all company data from a single repository. This organization is critical to the quality of the data that is ingested by analytics software.