Alternatives to Spark Streaming
Compare Spark Streaming alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Spark Streaming in 2026. Compare features, ratings, user reviews, pricing, and more from Spark Streaming competitors and alternatives in order to make an informed decision for your business.
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Samza
Apache Software Foundation
Samza allows you to build stateful applications that process data in real-time from multiple sources including Apache Kafka. Battle-tested at scale, it supports flexible deployment options to run on YARN or as a standalone library. Samza provides extremely low latencies and high throughput to analyze your data instantly. Scales to several terabytes of state with features like incremental checkpoints and host-affinity. Samza is easy to operate with flexible deployment options - YARN, Kubernetes or standalone. Ability to run the same code to process both batch and streaming data. Integrates with several sources including Kafka, HDFS, AWS Kinesis, Azure Eventhubs, K-V stores and ElasticSearch. -
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ksqlDB
Confluent
Now that your data is in motion, it’s time to make sense of it. Stream processing enables you to derive instant insights from your data streams, but setting up the infrastructure to support it can be complex. That’s why Confluent developed ksqlDB, the database purpose-built for stream processing applications. Make your data immediately actionable by continuously processing streams of data generated throughout your business. ksqlDB’s intuitive syntax lets you quickly access and augment data in Kafka, enabling development teams to seamlessly create real-time innovative customer experiences and fulfill data-driven operational needs. ksqlDB offers a single solution for collecting streams of data, enriching them, and serving queries on new derived streams and tables. That means less infrastructure to deploy, maintain, scale, and secure. With less moving parts in your data architecture, you can focus on what really matters -- innovation. -
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Apache Spark
Apache Software Foundation
Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources. -
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PySpark
PySpark
PySpark is an interface for Apache Spark in Python. It not only allows you to write Spark applications using Python APIs, but also provides the PySpark shell for interactively analyzing your data in a distributed environment. PySpark supports most of Spark’s features such as Spark SQL, DataFrame, Streaming, MLlib (Machine Learning) and Spark Core. Spark SQL is a Spark module for structured data processing. It provides a programming abstraction called DataFrame and can also act as distributed SQL query engine. Running on top of Spark, the streaming feature in Apache Spark enables powerful interactive and analytical applications across both streaming and historical data, while inheriting Spark’s ease of use and fault tolerance characteristics. -
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MLlib
Apache Software Foundation
Apache Spark's MLlib is a scalable machine learning library that integrates seamlessly with Spark's APIs, supporting Java, Scala, Python, and R. It offers a comprehensive suite of algorithms and utilities, including classification, regression, clustering, collaborative filtering, and tools for constructing machine learning pipelines. MLlib's high-quality algorithms leverage Spark's iterative computation capabilities, delivering performance up to 100 times faster than traditional MapReduce implementations. It is designed to operate across diverse environments, running on Hadoop, Apache Mesos, Kubernetes, standalone clusters, or in the cloud, and accessing various data sources such as HDFS, HBase, and local files. This flexibility makes MLlib a robust solution for scalable and efficient machine learning tasks within the Apache Spark ecosystem. -
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Azure Databricks
Microsoft
Unlock insights from all your data and build artificial intelligence (AI) solutions with Azure Databricks, set up your Apache Spark™ environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn. Azure Databricks provides the latest versions of Apache Spark and allows you to seamlessly integrate with open source libraries. Spin up clusters and build quickly in a fully managed Apache Spark environment with the global scale and availability of Azure. Clusters are set up, configured, and fine-tuned to ensure reliability and performance without the need for monitoring. Take advantage of autoscaling and auto-termination to improve total cost of ownership (TCO). -
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Deequ
Deequ
Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets. We are happy to receive feedback and contributions. Deequ depends on Java 8. Deequ version 2.x only runs with Spark 3.1, and vice versa. If you rely on a previous Spark version, please use a Deequ 1.x version (legacy version is maintained in legacy-spark-3.0 branch). We provide legacy releases compatible with Apache Spark versions 2.2.x to 3.0.x. The Spark 2.2.x and 2.3.x releases depend on Scala 2.11 and the Spark 2.4.x, 3.0.x, and 3.1.x releases depend on Scala 2.12. Deequ's purpose is to "unit-test" data to find errors early, before the data gets fed to consuming systems or machine learning algorithms. In the following, we will walk you through a toy example to showcase the most basic usage of our library. -
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Apache Mahout
Apache Software Foundation
Apache Mahout is a powerful, scalable, and versatile machine learning library designed for distributed data processing. It offers a comprehensive set of algorithms for various tasks, including classification, clustering, recommendation, and pattern mining. Built on top of the Apache Hadoop ecosystem, Mahout leverages MapReduce and Spark to enable data processing on large-scale datasets. Apache Mahout(TM) is a distributed linear algebra framework and mathematically expressive Scala DSL designed to let mathematicians, statisticians, and data scientists quickly implement their own algorithms. Apache Spark is the recommended out-of-the-box distributed back-end or can be extended to other distributed backends. Matrix computations are a fundamental part of many scientific and engineering applications, including machine learning, computer vision, and data analysis. Apache Mahout is designed to handle large-scale data processing by leveraging the power of Hadoop and Spark. -
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Managed Service for Apache Spark is a Google Cloud solution that simplifies running Apache Spark workloads with either serverless execution or fully managed clusters. It allows users to process large-scale data without needing to manage infrastructure, reducing operational complexity. The platform features Lightning Engine, which accelerates Spark performance by up to 4.9 times compared to open-source Spark. It supports data engineering, data science, and machine learning workflows at scale. Integration with Gemini enables AI-powered development, including automated code generation and troubleshooting. The service works seamlessly with open data formats like Apache Iceberg and integrates with tools like BigQuery and Knowledge Catalog. It offers flexible deployment options to suit different workloads and use cases. Overall, it provides a faster, smarter, and more efficient way to run Spark workloads in the cloud.
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Baidu AI Cloud Stream Computing
Baidu AI Cloud
Baidu Stream Computing (BSC) provides real-time streaming data processing capacity with low delay, high throughput and high accuracy. It is fully compatible with Spark SQL; and can realize the logic data processing of complicated businesses through SQL statement, which is easy to use; provides users with full life cycle management for the streaming-oriented computing jobs. Integrate deeply with multiple storage products of Baidu AI Cloud as the upstream and downstream of stream computing, including Baidu Kafka, RDS, BOS, IOT Hub, Baidu ElasticSearch, TSDB, SCS and others. Provide a comprehensive job monitoring indicator, and the user can view the monitoring indicators of the job and set the alarm rules to protect the job. -
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Amazon EMR
Amazon
Amazon EMR is the industry-leading cloud big data platform for processing vast amounts of data using open-source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto. With EMR you can run Petabyte-scale analysis at less than half of the cost of traditional on-premises solutions and over 3x faster than standard Apache Spark. For short-running jobs, you can spin up and spin down clusters and pay per second for the instances used. For long-running workloads, you can create highly available clusters that automatically scale to meet demand. If you have existing on-premises deployments of open-source tools such as Apache Spark and Apache Hive, you can also run EMR clusters on AWS Outposts. Analyze data using open-source ML frameworks such as Apache Spark MLlib, TensorFlow, and Apache MXNet. Connect to Amazon SageMaker Studio for large-scale model training, analysis, and reporting. -
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IBM Analytics for Apache Spark is a flexible and integrated Spark service that empowers data science professionals to ask bigger, tougher questions, and deliver business value faster. It’s an easy-to-use, always-on managed service with no long-term commitment or risk, so you can begin exploring right away. Access the power of Apache Spark with no lock-in, backed by IBM’s open-source commitment and decades of enterprise experience. A managed Spark service with Notebooks as a connector means coding and analytics are easier and faster, so you can spend more of your time on delivery and innovation. A managed Apache Spark services gives you easy access to the power of built-in machine learning libraries without the headaches, time and risk associated with managing a Sparkcluster independently.
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Oracle Cloud Infrastructure (OCI) Data Flow is a fully managed Apache Spark service to perform processing tasks on extremely large data sets without infrastructure to deploy or manage. This enables rapid application delivery because developers can focus on app development, not infrastructure management. OCI Data Flow handles infrastructure provisioning, network setup, and teardown when Spark jobs are complete. Storage and security are also managed, which means less work is required for creating and managing Spark applications for big data analysis. With OCI Data Flow, there are no clusters to install, patch, or upgrade, which saves time and operational costs for projects. OCI Data Flow runs each Spark job in private dedicated resources, eliminating the need for upfront capacity planning. With OCI Data Flow, IT only needs to pay for the infrastructure resources that Spark jobs use while they are running.Starting Price: $0.0085 per GB per hour
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BigBI
BigBI
BigBI enables data specialists to build their own powerful big data pipelines interactively & efficiently, without any coding! BigBI unleashes the power of Apache Spark enabling: Scalable processing of real Big Data (up to 100X faster) Integration of traditional data (SQL, batch files) with modern data sources including semi-structured (JSON, NoSQL DBs, Elastic, Hadoop), and unstructured (Text, Audio, video), Integration of streaming data, cloud data, AI/ML & graphs -
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Equalum
Equalum
Equalum’s continuous data integration & streaming platform is the only solution that natively supports real-time, batch, and ETL use cases under one, unified platform with zero coding required. Make the move to real-time with a fully orchestrated, drag-and-drop, no-code UI. Experience rapid deployment, powerful transformations, and scalable streaming data pipelines in minutes. Multi-modal, robust, and scalable CDC enabling real-time streaming and data replication. Tuned for best-in-class performance no matter the source. The power of open-source big data frameworks, without the hassle. Equalum harnesses the scalability of open-source data frameworks such as Apache Spark and Kafka in the Platform engine to dramatically improve the performance of streaming and batch data processes. Organizations can increase data volumes while improving performance and minimizing system impact using this best-in-class infrastructure. -
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Deeplearning4j
Deeplearning4j
DL4J takes advantage of the latest distributed computing frameworks including Apache Spark and Hadoop to accelerate training. On multi-GPUs, it is equal to Caffe in performance. The libraries are completely open-source, Apache 2.0, and maintained by the developer community and Konduit team. Deeplearning4j is written in Java and is compatible with any JVM language, such as Scala, Clojure, or Kotlin. The underlying computations are written in C, C++, and Cuda. Keras will serve as the Python API. Eclipse Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Apache Spark, DL4J brings AI to business environments for use on distributed GPUs and CPUs. There are a lot of parameters to adjust when you're training a deep-learning network. We've done our best to explain them, so that Deeplearning4j can serve as a DIY tool for Java, Scala, Clojure, and Kotlin programmers. -
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E-MapReduce
Alibaba
EMR is an all-in-one enterprise-ready big data platform that provides cluster, job, and data management services based on open-source ecosystems, such as Hadoop, Spark, Kafka, Flink, and Storm. Alibaba Cloud Elastic MapReduce (EMR) is a big data processing solution that runs on the Alibaba Cloud platform. EMR is built on Alibaba Cloud ECS instances and is based on open-source Apache Hadoop and Apache Spark. EMR allows you to use the Hadoop and Spark ecosystem components, such as Apache Hive, Apache Kafka, Flink, Druid, and TensorFlow, to analyze and process data. You can use EMR to process data stored on different Alibaba Cloud data storage service, such as Object Storage Service (OSS), Log Service (SLS), and Relational Database Service (RDS). You can quickly create clusters without the need to configure hardware and software. All maintenance operations are completed on its Web interface. -
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Google Cloud Dataflow
Google
Unified stream and batch data processing that's serverless, fast, and cost-effective. Fully managed data processing service. Automated provisioning and management of processing resources. Horizontal autoscaling of worker resources to maximize resource utilization. OSS community-driven innovation with Apache Beam SDK. Reliable and consistent exactly-once processing. Streaming data analytics with speed. Dataflow enables fast, simplified streaming data pipeline development with lower data latency. Allow teams to focus on programming instead of managing server clusters as Dataflow’s serverless approach removes operational overhead from data engineering workloads. Allow teams to focus on programming instead of managing server clusters as Dataflow’s serverless approach removes operational overhead from data engineering workloads. Dataflow automates provisioning and management of processing resources to minimize latency and maximize utilization. -
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LakeSail
LakeSail
LakeSail is a unified, cloud-native data and AI platform designed to transform how organizations process, analyze, and act on large-scale data by combining all workloads into a single, high-performance system. At its core is Sail, a Rust-native distributed computation engine that serves as a drop-in replacement for Apache Spark, enabling teams to run existing SQL and Python workloads without rewriting code while eliminating JVM overhead and improving efficiency. It unifies batch processing, stream processing, ad-hoc queries, and AI workloads into one runtime, allowing data pipelines and intelligent systems to operate seamlessly on the same infrastructure. It introduces a multimodal lakehouse architecture capable of handling structured and unstructured data, including PDFs, images, and video, within a single environment, making it suitable for modern AI-driven use cases. -
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Apache Kafka
The Apache Software Foundation
Apache Kafka® is an open-source, distributed streaming platform. Scale production clusters up to a thousand brokers, trillions of messages per day, petabytes of data, hundreds of thousands of partitions. Elastically expand and contract storage and processing. Stretch clusters efficiently over availability zones or connect separate clusters across geographic regions. Process streams of events with joins, aggregations, filters, transformations, and more, using event-time and exactly-once processing. Kafka’s out-of-the-box Connect interface integrates with hundreds of event sources and event sinks including Postgres, JMS, Elasticsearch, AWS S3, and more. Read, write, and process streams of events in a vast array of programming languages. -
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Astra Streaming
DataStax
Responsive applications keep users engaged and developers inspired. Rise to meet these ever-increasing expectations with the DataStax Astra Streaming service platform. DataStax Astra Streaming is a cloud-native messaging and event streaming platform powered by Apache Pulsar. Astra Streaming allows you to build streaming applications on top of an elastically scalable, multi-cloud messaging and event streaming platform. Astra Streaming is powered by Apache Pulsar, the next-generation event streaming platform which provides a unified solution for streaming, queuing, pub/sub, and stream processing. Astra Streaming is a natural complement to Astra DB. Using Astra Streaming, existing Astra DB users can easily build real-time data pipelines into and out of their Astra DB instances. With Astra Streaming, avoid vendor lock-in and deploy on any of the major public clouds (AWS, GCP, Azure) compatible with open-source Apache Pulsar. -
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WarpStream
WarpStream
WarpStream is an Apache Kafka-compatible data streaming platform built directly on top of object storage, with no inter-AZ networking costs, no disks to manage, and infinitely scalable, all within your VPC. WarpStream is deployed as a stateless and auto-scaling agent binary in your VPC with no local disks to manage. Agents stream data directly to and from object storage with no buffering on local disks and no data tiering. Create new “virtual clusters” in our control plane instantly. Support different environments, teams, or projects without managing any dedicated infrastructure. WarpStream is protocol compatible with Apache Kafka, so you can keep using all your favorite tools and software. No need to rewrite your application or use a proprietary SDK. Just change the URL in your favorite Kafka client library and start streaming. Never again have to choose between reliability and your budget.Starting Price: $2,987 per month -
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GitHub Spark
GitHub Spark
We can enable anyone to create or adapt software for themselves, using AI and a fully-managed runtime. GitHub Spark is an AI-powered tool for creating and sharing micro apps (“sparks”), which can be tailored to your exact needs and preferences, and are directly usable from your desktop and mobile devices. Without needing to write or deploy any code. It enables this through a combination of three tightly integrated components. An NL-based editor, which allows easily describe your ideas, and then refine them over time. A managed runtime environment, which hosts your sparks, and provides them access to data storage, theming, and LLMs. A PWA-enabled dashboard, which lets you manage and launch your sparks from anywhere. Additionally, GitHub Spark allows you to share your sparks with others, and control whether they get read-only or read-write permissions. They can then choose to favorite the spark, and use it directly, or remix it, in order to further adapt it to their preferences. -
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Spark NLP
John Snow Labs
Experience the power of large language models like never before, unleashing the full potential of Natural Language Processing (NLP) with Spark NLP, the open source library that delivers scalable LLMs. The full code base is open under the Apache 2.0 license, including pre-trained models and pipelines. The only NLP library built natively on Apache Spark. The most widely used NLP library in the enterprise. Spark ML provides a set of machine learning applications that can be built using two main components, estimators and transformers. The estimators have a method that secures and trains a piece of data to such an application. The transformer is generally the result of a fitting process and applies changes to the target dataset. These components have been embedded to be applicable to Spark NLP. Pipelines are a mechanism for combining multiple estimators and transformers in a single workflow. They allow multiple chained transformations along a machine-learning task.Starting Price: Free -
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Beaker Notebook
Two Sigma Open Source
BeakerX is a collection of kernels and extensions to the Jupyter interactive computing environment. It provides JVM support, Spark cluster support, polyglot programming, interactive plots, tables, forms, publishing, and more. All of BeakerX’s JVM languages plus Python and JavaScript have APIs for interactive time-series, scatter plots, histograms, heatmaps, and treemaps. The widgets remain interactive in both notebooks saved to disk, and notebooks published to the web. They include unique features for handling many points, nanosecond resolution, zooming, and exporting. BeakerX’s table widget automatically recognizes pandas data frames and allows you to search, sort, drag, filter, format, select, graph, hide, pin, and export to CSV or clipboard. This makes connecting to spreadsheets quickly and easy. BeakerX has a Spark magic with GUIs for configuration, status, progress, and interrupt of Spark jobs. You can either use the GUI or create your own SparkSession with code. -
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Streaming service is a real-time, serverless, Apache Kafka-compatible event streaming platform for developers and data scientists. Streaming is tightly integrated with Oracle Cloud Infrastructure (OCI), Database, GoldenGate, and Integration Cloud. The service also provides out-of-the-box integrations for hundreds of third-party products across categories such as DevOps, databases, big data, and SaaS applications. Data engineers can easily set up and operate big data pipelines. Oracle handles all infrastructure and platform management for event streaming, including provisioning, scaling, and security patching. With the help of consumer groups, Streaming can provide state management for thousands of consumers. This helps developers easily build applications at scale.
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Spark Voicemail
Spark
Spark Voicemail revolutionises your voicemail experience, making it effortless to retrieve and respond to voicemails. Spark Pay Monthly mobile users can install and use the Spark Voicemail app for free as part of their plan. Spark Prepay users need to activate the ‘Voicemail Unlimited’ extra for $1 per 4 weeks, which offers unlimited App and Voicemail use. So you can boost your responsiveness by also sending voicemails to your assistant or team to respond on your behalf! Don't worry; you can filter out calls from personal contacts. With our built-in automatic transcription service, Spark Voicemail makes your voicemails effortlessly searchable. Spark Voicemail lets you easily record a new one. Change it every season, or if you're away on holiday.Starting Price: Free -
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DeltaStream
DeltaStream
DeltaStream is a unified serverless stream processing platform that integrates with streaming storage services. Think about it as the compute layer on top of your streaming storage. It provides functionalities of streaming analytics(Stream processing) and streaming databases along with additional features to provide a complete platform to manage, process, secure and share streaming data. DeltaStream provides a SQL based interface where you can easily create stream processing applications such as streaming pipelines, materialized views, microservices and many more. It has a pluggable processing engine and currently uses Apache Flink as its primary stream processing engine. DeltaStream is more than just a query processing layer on top of Kafka or Kinesis. It brings relational database concepts to the data streaming world, including namespacing and role based access control enabling you to securely access, process and share your streaming data regardless of where they are stored. -
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ReSpark
ReSpark
ReSpark is the definitive software platform for metal recyclers, built to connect every part of the yard in one system. From scale ticketing and inventory to pricing, dispatch, exports, finance, reporting, and AI-powered workflows, ReSpark helps scrap yards run faster, cleaner, and with better visibility across the business. Whether you’re managing one facility or a multi-yard operation, ReSpark gives your team the tools to buy, process, sell, track, and reconcile material with less manual work and more confidence. Purpose-built for scrap, ReSpark brings together the best of ReMatter and GreenSpark to support 800+ scrap companies across 1,000+ facilities with a connected platform designed for how recyclers actually operate. -
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Study Fetch
StudyFetch
StudyFetch is a revolutionary new platform that allows you to upload your course materials and create interactive study sets. You can study with an AI tutor, create flashcards, generate notes, take practice tests, and more. Spark.e, our AI tutor, allows you to interact directly with your study materials. You can ask questions, create flashcards, take practice tests, and customize your learning experience. StudyFetch's AI, Spark.e, utilizes advanced machine learning algorithms to offer a tailored, interactive tutoring experience. Once you upload your study materials, Spark.e scans and indexes them, making the content searchable and accessible for real-time queries. -
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IOMETE
IOMETE
IOMETE is a self-hosted data lakehouse platform built on Apache Iceberg, Apache Spark, and Kubernetes. Run it on-premises or in your private cloud — your infrastructure, your data, your control. Built for enterprises in regulated industries, IOMETE eliminates third-party ICT risk at the data layer by architecture — not by contract. No SaaS dependencies. No data leaving your perimeter. Compliance with GDPR, DORA, and NIS2 is structural, not contractual. Included in one platform: - Data Lakehouse(s) - Data Catalog - SQL Editor - Apache Spark Jobs - ML Notebooks - Orchestration Engine - Spark Connect Key capabilities: Apache Iceberg-native storage, Kubernetes-native deployment (K8s + OpenShift), row/column/tag-based access control, Data Mesh support, air-gapped and zero-trust compatible. Transparent pricing — CPU-based, no per-query fees, no billing surprises.Starting Price: Free -
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Pepperdata
Pepperdata, Inc.
Pepperdata autonomous cost optimization for data-intensive workloads such as Apache Spark is the only solution that delivers 30-47% greater cost savings continuously and in real time with no application changes or manual tuning. Deployed on over 20,000+ clusters, Pepperdata Capacity Optimizer provides resource optimization and full-stack observability in some of the largest and most complex environments in the world, enabling customers to run Spark on 30% less infrastructure on average. In the last decade, Pepperdata has helped top enterprises such as Citibank, Autodesk, Royal Bank of Canada, members of the Fortune 10, and mid-sized companies save over $250 million. -
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Muse Spark 1.1
Meta
Muse Spark 1.1 is a multimodal reasoning model from Meta Superintelligence Labs built for agentic tasks, coding, computer use, tool use, and multimodal understanding. The model improves on the original Muse Spark with stronger performance in planning, orchestration, long-context work, coding workflows, and external app interactions. Muse Spark 1.1 can manage a 1 million token context window, remember earlier actions, retrieve important information, compact context, and delegate tasks across parallel subagents. It is designed to operate across tools, MCP servers, custom skills, browsers, native apps, scripts, images, video, PDFs, and audio-based workflows. Developers can access Muse Spark 1.1 through the new Meta Model API public preview, while users can try it in Thinking mode in the Meta AI app and on meta.ai.Starting Price: $1.25 per 1M tokens (input) -
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Muse Spark
Meta
Muse Spark is a multimodal AI reasoning model developed by Meta as part of its push toward personal superintelligence. It integrates text, images, and tools to deliver advanced reasoning and interactive capabilities. The model supports features like visual chain-of-thought and multi-agent orchestration. Users can leverage Muse Spark for tasks such as problem-solving, content creation, and real-world troubleshooting. Its Contemplating mode enables multiple AI agents to reason in parallel for improved performance. Muse Spark also demonstrates strong capabilities in areas like health insights and visual understanding. Overall, it represents a significant step toward more intelligent and personalized AI systems. -
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Apache PredictionIO
Apache
Apache PredictionIO® is an open-source machine learning server built on top of a state-of-the-art open-source stack for developers and data scientists to create predictive engines for any machine learning task. It lets you quickly build and deploy an engine as a web service on production with customizable templates. Respond to dynamic queries in real-time once deployed as a web service, evaluate and tune multiple engine variants systematically, and unify data from multiple platforms in batch or in real-time for comprehensive predictive analytics. Speed up machine learning modeling with systematic processes and pre-built evaluation measures, support machine learning and data processing libraries such as Spark MLLib and OpenNLP. Implement your own machine learning models and seamlessly incorporate them into your engine. Simplify data infrastructure management. Apache PredictionIO® can be installed as a full machine learning stack, bundled with Apache Spark, MLlib, HBase, Akka HTTP, etc.Starting Price: Free -
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IBM Event Streams is a fully managed event streaming platform built on Apache Kafka, designed to help enterprises process and respond to real-time data streams. With capabilities for machine learning integration, high availability, and secure cloud deployment, it enables organizations to create intelligent applications that react to events as they happen. The platform supports multi-cloud environments, disaster recovery, and geo-replication, making it ideal for mission-critical workloads. IBM Event Streams simplifies building and scaling real-time, event-driven solutions, ensuring data is processed quickly and efficiently.
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Walmart Spark
Walmart
Available in more than 600 cities, Spark Driver makes it possible for service providers to earn money by shopping and delivering customer orders from Walmart and other retailers. It’s simple: customers place their orders online; orders are distributed to service providers through the Spark Driver App, and service providers accept to complete the order delivery! Flexibility, convenience, and simplicity, all you need is a car and a phone! Visit the Join Spark Driver tab on the Spark Driver website to view the service area map, select your preferred area, and complete the enrollment form. Once your information has been submitted for review, you will receive a confirmation email from our third-party administrator, Delivery Drivers, Inc. (DDI), which will provide details on how to complete the enrollment and create your Spark Driver account. Background check results are typically available within 2-7 business days, depending on state and county processes. -
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ReSpark
ReSpark
ReSpark is a professional, cloud-based salon and spa software built for modern beauty businesses. Whether you run a hair salon, spa, or beauty clinic, ReSpark helps you simplify daily operations, improve staff efficiency, and boost overall profits. From appointments to payments, marketing to inventory—ReSpark automates it all so you can focus on what matters most: your clients. ReSpark is an all-in-one salon management system that includes POS & Billing, Online Appointments & Dashboard, CRM & Client Profiles, Memberships & Packages, E-Commerce Integration, Inventory Management, Digital Catalog, Campaign Creator & WhatsApp Marketing, Feedback & Loyalty Programs, and Advanced Reports & Analytics. ReSpark salon software supports everything you need—from managing daily tasks to scaling your business online. -
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SparkInfluence
SparkInfluence
SparkInfluence helps the most successful government affairs and public relations teams better educate, engage, and empower their networks to act. SparkInfluence is an all-in-one, mobile-friendly software platform with the most advanced toolset on the market. Build your data-driven effort today and start getting the most out of your audience. SparkInfluence is a simple, easy-to-use software to help you build a better advocacy effort, PAC, or online community. Combining the best of grassroots advocacy tools alongside fundraising, CRM, PAC, grasstops, and more, SparkInfluence has all the functionality you need to track, manage, educate, engage, and empower your audience. Each product in the software platform is powerful on its own, but the real magic happens when you combine them together. SparkPAC is the most advanced PAC software on the market. -
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WebSparks
WebSparks.AI
WebSparks is an AI-powered platform that enables users to transform ideas into production-ready applications swiftly and efficiently. By interpreting text descriptions, images, and sketches, it generates complete full-stack applications featuring responsive frontends, robust backends, and optimized databases. With real-time previews and one-click deployment, WebSparks streamlines the development process, making it accessible to developers, designers, and non-coders alike. WebSparks is a full-stack AI software engineer.Starting Price: $15/month -
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Available in IBM Watson® Studio and Watson™ Knowledge Catalog, the data refinery tool saves data preparation time by quickly transforming large amounts of raw data into consumable, quality information that’s ready for analytics. Interactively discover, cleanse, and transform your data with over 100 built-in operations. No coding skills are required. Understand the quality and distribution of your data using dozens of built-in charts, graphs, and statistics. Automatically detect data types and business classifications. Access and explore data residing in a wide spectrum of data sources within your organization or the cloud. Automatically enforce policies set by data governance professionals. Schedule data flow executions for repeatable outcomes. Monitor results and receive notifications. Easily scale out via Apache Spark to apply transformation recipes on full data sets. No management of Apache Spark clusters needed.
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SparkLoop
SparkLoop
Thousands of smart newsletter creators use SparkLoop to get more, high-quality email subscribers on autopilot. You should too. With SparkLoop it's easy to reward your subscribers for sharing your newsletter with their friends. So you grow faster, improve subscriber engagement, and spend less money and time on growth. Unlike other referral tools, SparkLoop was built for newsletters. So you can set up your powerful referral program, exactly like Morning Brew, in just a few clicks. No developers, code or Zapier hacks needed! Give all your subscribers a unique referral link, right inside your newsletter. Incentivize your subscribers to share their referral link with rewards and giveaways. Watch your audience grow your email-list for you, from your SparkLoop dashboard. The biggest and best newsletters on the web trust SparkLoop to help them grow. With advanced fraud prevention, full white-label and enterprise-grade security, we're the only solution you can trust.Starting Price: $99 per month -
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GuideSpark
GuideSpark
GuideSpark is the leader in change communications guiding over 1,000 enterprise customers to business success by changing the hearts and minds of employees. GuideSpark Communicate Cloud® drives organizational change with communication journeys, targeted experiences that reach, engage and change employee behavior to achieve your critical business goals. Manage, measure and scale your internal communications effectiveness with GuideSpark. -
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Arroyo
Arroyo
Scale from zero to millions of events per second. Arroyo ships as a single, compact binary. Run locally on MacOS or Linux for development, and deploy to production with Docker or Kubernetes. Arroyo is a new kind of stream processing engine, built from the ground up to make real-time easier than batch. Arroyo was designed from the start so that anyone with SQL experience can build reliable, efficient, and correct streaming pipelines. Data scientists and engineers can build end-to-end real-time applications, models, and dashboards, without a separate team of streaming experts. Transform, filter, aggregate, and join data streams by writing SQL, with sub-second results. Your streaming pipelines shouldn't page someone just because Kubernetes decided to reschedule your pods. Arroyo is built to run in modern, elastic cloud environments, from simple container runtimes like Fargate to large, distributed deployments on the Kubernetes logo Kubernetes. -
45
Meta Model API
Meta
Meta Model API is a new developer API for building with Muse Spark 1.1, Meta’s multimodal reasoning model built for agentic tasks, coding, tool use, computer use, and multimodal understanding. Now in public preview, it gives developers a way to access Muse Spark 1.1 through an OpenAI-compatible package, making it easier to point existing clients at the API, keep the same code structure, and set the model to muse-spark-1.1. Muse Spark 1.1 is designed for personal agentic tasks that require planning and orchestration across external apps and services, with the ability to generalize to new native tools, MCP servers, and custom skills. As a main agent, it can gather context, make a plan, and delegate execution across parallel subagents; as a subagent, it follows its role, understands available tools, and knows when to escalate back. The model can actively manage a 1 million-token context window, remember actions, retrieve information from much earlier work, and compact context.Starting Price: $1.25 per 1M tokens -
46
Azure HDInsight
Microsoft
Run popular open-source frameworks—including Apache Hadoop, Spark, Hive, Kafka, and more—using Azure HDInsight, a customizable, enterprise-grade service for open-source analytics. Effortlessly process massive amounts of data and get all the benefits of the broad open-source project ecosystem with the global scale of Azure. Easily migrate your big data workloads and processing to the cloud. Open-source projects and clusters are easy to spin up quickly without the need to install hardware or manage infrastructure. Big data clusters reduce costs through autoscaling and pricing tiers that allow you to pay for only what you use. Enterprise-grade security and industry-leading compliance with more than 30 certifications helps protect your data. Optimized components for open-source technologies such as Hadoop and Spark keep you up to date. -
47
Spark
RebelWare
Spark is our fully customizable landing page-builder that presents content in a format tailor-made for specific audiences across a broad range of applications — contact forms, sales enablement, welcome, and onboarding. We created Spark to deliver one thing really well: send information to key audiences in a fast, consistent, branded, engaging, and trackable way. Spark places all of your sales engagement materials directly in your sales team’s hands, the lag time in waiting for a response. Spark can help in any situation that requires quick, customizable presentation of documents, including sales, marketing, training, compliance, HR and more. -
48
sparkPRO
Quality Early Years
sparkPRO is designed to be efficient and promote the well-being of a team in any setting. sparkPRO is more than a learning journey; the features support your team with the Early Year Foundation Stage and curriculum delivery. A leading EYFS curriculum software package, sparkPRO organizes staff time, systematizes procedures, provides ongoing EYFS assessment with a focus on quality during delivery. It provides incredible financial savings, operationally by cutting down on planning, observation, assessment and recording times. Tangibly you will save on ink and paper costs. sparkPRO not only incorporates the whole of our sparkESSENTIAL package, it also includes additional features and advanced reporting options. Supports whole team to deliver a curriculum and ‘get it right’ for each child, assessment, planning, recording and evaluating personal practice. Support your staff welfare, manage time, increase standards, allow more time to meet individual needs. -
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Spark.work
Spark.work
Spark.work is a platform that unites HR Management (HRMS) and Strategy Execution. Designed for growing companies, Spark helps leaders gain clarity and efficiency in people operations, then leverages that foundation to align and execute strategy across the organization. What Spark.work Offers Spark simplifies HR processes while connecting them directly to business goals: People Management: Centralized employee data, leave and attendance tracking, onboarding/offboarding workflows, document management, and visual organization charts. Talent & Growth: Applicant Tracking System (ATS), performance reviews, employee feedback, and development planning. Strategy & Performance: Strategy maps, OKRs, KPIs, and initiatives — all linked back to people and teams. AI Assistance: Smart agents that support KPI/OKR setup, surface insights, and automate repetitive tasks.Starting Price: $1.5 month/per user -
50
Nussknacker
Nussknacker
Nussknacker is a low-code visual tool for domain experts to define and run real-time decisioning algorithms instead of implementing them in the code. It serves where real-time actions on data have to be made: real-time marketing, fraud detection, Internet of Things, Customer 360, and Machine Learning inferring. An essential part of Nussknacker is a visual design tool for decision algorithms. It allows not-so-technical users – analysts or business people – to define decision logic in an imperative, easy-to-follow, and understandable way. Once authored, with a click of a button, scenarios are deployed for execution. And can be changed and redeployed anytime there’s a need. Nussknacker supports two processing modes: streaming and request-response. In streaming mode, it uses Kafka as its primary interface. It supports both stateful and stateless processing.Starting Price: 0
