Alternatives to ZetaAnalytics
Compare ZetaAnalytics alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to ZetaAnalytics in 2026. Compare features, ratings, user reviews, pricing, and more from ZetaAnalytics competitors and alternatives in order to make an informed decision for your business.
-
1
ZetaSafe
ZetaSafe
Whether you are a service provider or managing your own compliance. ZetaSafe can help you meet compliance obligations, keeping your people and buildings safe and secure. We are delighted to announce that Micad has acquired Zeta Compliance Technologies LLC (SEER), the reseller of ZetaSafe software in North America. Track and manage compliance obligations with ease and accuracy. Increased control of data, costs, labor and processes. Robust audit trail and visible reassurance that all required monitoring has been completed on time. Real-time visibility of data, KPIs and trends. Auto escalation of non-compliances enables proactive and rapid management of failures or defects. HSG274, HTMs, (HTM 04:01), the RRO Fire Safety 2005 and SFG20. Book a Demo to find out how ZetaSafe really works. Scan barcodes, add compliance data, and see trends and reports! Work collaboratively with you to assess your compliance management processes and configure ZetaSafe to ensure compliance.Starting Price: $500.00/month -
2
Zeta CDP+
Zeta
Zeta CDP+ provides marketers with more growth opportunities than a traditional CDP. Connect all consumer data, known and unknown, into one real-time, enhanced view that continuously translates their behaviors into actionable marketing insights. We put the plus in CDP+ for a reason. Our goal is to partner with progressive marketers to grow revenue, build better customer experiences, and support digital transformation strategies. CDP + enriches marketers’ data with 2,500+ intent and interest signals, identity resolution, syndication, and activation from Zeta’s Data Cloud to deliver a more actionable, real-time view of both prospects and customers, at the individual level. Deploy personalized messaging that actually resonates. Zeta CDP+ is the only customer data management solution that delivers up to the minute interests and real-time intent signals that you can quickly activate on, resulting in higher engagement and better results. -
3
Zeta SSP
Zeta
Introducing the Zeta SSP—connecting publishers to Zeta’s vast network of brands and advertisers through direct supply partnerships. Access standard IAB category blocking, domain-level advertiser blocking, and ad-format-and-sizing optimizations. Inform your strategy with robust performance metrics, demand buying insights, and targeted filtering criteria. The Zeta SSP leverages our in-house DSP, years of partnerships with premier digital marketers, and strategic, third-party DSPs to help maximize demand for your supply and drive meaningful revenue growth. Direct account management and access to diverse network of dedicated support channels. Leverage our Disqus network to own your user data and future-proof your site's authentication strategy as we prepare for the inevitable cookie-less environment. Quick and easy implementation. Direct integrations with Prebid, oRTB, and VAST. -
4
Big Zeta Product Configurator
Big Zeta
Big Zeta’s product configurator offers a great solution for any product structure in which a visual might enhance the decision making process. Give your customers a guided journey flow that provides more contextual search capabilities than ever before. Even if you lack the resources to manage complex routing logic or great UI/UX, your customers deserve a product configurator designed by industry experts. Let Big Zeta help you fit a top of the line product configurator into your buyer’s journey. Design analytics are important for growth, and our product configurator offers the best, helping you understand what drives your customers toward the outcomes of your choosing. We’re focused on growing your company by providing a guided customer experience that is second to none. Allow your customers to move from general to acutely specific search queries with a product configuration wizard that easily integrates with parametric search and keyword search, CMS, PIM, MAP, and CRM. -
5
Big Zeta Keyword Search
Big Zeta
Built to meet the complex needs of B2B companies, Big Zeta Keyword Search is easy to deploy and maintain, while offering sophisticated management and reporting of your search program. Stop worrying about whether your search results are unreliable or your user experience too slow. Our cutting-edge technology simply delivers. Start prioritizing site search. With our leading-edge functionality and robust analytics platform, you can finally make keyword search a critical part of your digital strategy. Big Zeta keyword search propels fast finding for your customers, offering correct context leveraging from multiple data sources, an easy to use interface, and correct results in the right time and place. Maximize Big Zeta Keyword Search via a site crawl or through connectors into your content and product systems. Keep your results up to date with automated refreshes. Know that your site is displaying the latest results. -
6
Zeta Marketing Platform
Zeta Global
Zeta Global is a data-powered marketing technology company that combines the industry’s third largest data set (2.4B+ identities) with results-driven AI to unlock consumer intent, personalize experiences and drive customer acquisition, retention and growth. Everything a marketer needs, from acquisition through retention. Learn who to target and create experiences across every channel to deliver real-time personalization at scale, driving superior outcomes. The Zeta Marketing Platform provides a real-time view of prospects and customers, leveraging AI to create personalization in every channel at unparalleled depth and scale. Our rare mix of capabilities has been helping brands grow for over a decade. -
7
Zeta Alpha
Zeta Alpha
Zeta Alpha is the best Neural Discovery Platform for AI and beyond. Use state-of-the-art Neural Search to improve how you and your team discover, organize and share knowledge. Make better decisions, avoid reinventing the wheel, and make staying in the know effortless: the power of modern AI to make an impact with your work faster. With state-of-the-art neural discovery across all relevant AI research and engineering information sources. Ensure that nothing falls through the cracks with a seamless combination of powerful search, organization, and recommendation features. Steer decision-making across the organization and reduce associated risks by maintaining a unified view of relevant internal and external information. Get a clear overview of what your team is reading and working on.Starting Price: €20 per month -
8
Jace
Zeta Labs
Meet your new AI assistant and focus on meaningful things. A groundbreaking digital assistant, JACE represents the future of AI agents, going beyond traditional uses of current AI chatbots like ChatGPT and their text-generation focus. Instead, JACE focuses on taking action in the digital world. It differs from existing AI-powered chatbots due to its complex cognitive architecture, which enables it to complete high-difficulty tasks. JACE can control and perform actions in the browser similarly to a human user, excelling in managing complex tasks that involve web automation, interaction, and direct communication. This is due to the development and training of Zeta Labs’ proprietary web-interaction model, AWA-1 (Autonomous Web Agent-1), which enables JACE to reliably execute tasks over long periods of time, effectively handling the challenges and inconsistencies commonly found in web interfaces.Starting Price: $20 per month -
9
Apache Kylin
Apache Software Foundation
Apache Kylin™ is an open source, distributed Analytical Data Warehouse for Big Data; it was designed to provide OLAP (Online Analytical Processing) capability in the big data era. By renovating the multi-dimensional cube and precalculation technology on Hadoop and Spark, Kylin is able to achieve near constant query speed regardless of the ever-growing data volume. Reducing query latency from minutes to sub-second, Kylin brings online analytics back to big data. Kylin can analyze 10+ billions of rows in less than a second. No more waiting on reports for critical decisions. Kylin connects data on Hadoop to BI tools like Tableau, PowerBI/Excel, MSTR, QlikSense, Hue and SuperSet, making the BI on Hadoop faster than ever. As an Analytical Data Warehouse, Kylin offers ANSI SQL on Hadoop/Spark and supports most ANSI SQL query functions. Kylin can support thousands of interactive queries at the same time, thanks to the low resource consumption of each query. -
10
IBM Analytics Engine provides an architecture for Hadoop clusters that decouples the compute and storage tiers. Instead of a permanent cluster formed of dual-purpose nodes, the Analytics Engine allows users to store data in an object storage layer such as IBM Cloud Object Storage and spins up clusters of computing notes when needed. Separating compute from storage helps to transform the flexibility, scalability and maintainability of big data analytics platforms. Build on an ODPi compliant stack with pioneering data science tools with the broader Apache Hadoop and Apache Spark ecosystem. Define clusters based on your application's requirements. Choose the appropriate software pack, version, and size of the cluster. Use as long as required and delete as soon as an application finishes jobs. Configure clusters with third-party analytics libraries and packages. Deploy workloads from IBM Cloud services like machine learning.Starting Price: $0.014 per hour
-
11
Apache Sentry
Apache Software Foundation
Apache Sentry™ is a system for enforcing fine grained role based authorization to data and metadata stored on a Hadoop cluster. Apache Sentry has successfully graduated from the Incubator in March of 2016 and is now a Top-Level Apache project. Apache Sentry is a granular, role-based authorization module for Hadoop. Sentry provides the ability to control and enforce precise levels of privileges on data for authenticated users and applications on a Hadoop cluster. Sentry currently works out of the box with Apache Hive, Hive Metastore/HCatalog, Apache Solr, Impala and HDFS (limited to Hive table data). Sentry is designed to be a pluggable authorization engine for Hadoop components. It allows you to define authorization rules to validate a user or application’s access requests for Hadoop resources. Sentry is highly modular and can support authorization for a wide variety of data models in Hadoop. -
12
Hadoop
Apache Software Foundation
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures. A wide variety of companies and organizations use Hadoop for both research and production. Users are encouraged to add themselves to the Hadoop PoweredBy wiki page. Apache Hadoop 3.3.4 incorporates a number of significant enhancements over the previous major release line (hadoop-3.2). -
13
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. -
14
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. -
15
Greenplum
Greenplum Database
Greenplum Database® is an advanced, fully featured, open source data warehouse. It provides powerful and rapid analytics on petabyte scale data volumes. Uniquely geared toward big data analytics, Greenplum Database is powered by the world’s most advanced cost-based query optimizer delivering high analytical query performance on large data volumes. Greenplum Database® project is released under the Apache 2 license. We want to thank all our current community contributors and are interested in all new potential contributions. For the Greenplum Database community no contribution is too small, we encourage all types of contributions. An open-source massively parallel data platform for analytics, machine learning and AI. Rapidly create and deploy models for complex applications in cybersecurity, predictive maintenance, risk management, fraud detection, and many other areas. Experience the fully featured, integrated, open source analytics platform. -
16
Apache Phoenix
Apache Software Foundation
Apache Phoenix enables OLTP and operational analytics in Hadoop for low-latency applications by combining the best of both worlds. The power of standard SQL and JDBC APIs with full ACID transaction capabilities and the flexibility of late-bound, schema-on-read capabilities from the NoSQL world by leveraging HBase as its backing store. Apache Phoenix is fully integrated with other Hadoop products such as Spark, Hive, Pig, Flume, and Map Reduce. Become the trusted data platform for OLTP and operational analytics for Hadoop through well-defined, industry-standard APIs. Apache Phoenix takes your SQL query, compiles it into a series of HBase scans, and orchestrates the running of those scans to produce regular JDBC result sets. Direct use of the HBase API, along with coprocessors and custom filters, results in performance on the order of milliseconds for small queries, or seconds for tens of millions of rows.Starting Price: Free -
17
Oracle Big Data SQL Cloud Service enables organizations to immediately analyze data across Apache Hadoop, NoSQL and Oracle Database leveraging their existing SQL skills, security policies and applications with extreme performance. From simplifying data science efforts to unlocking data lakes, Big Data SQL makes the benefits of Big Data available to the largest group of end users possible. Big Data SQL gives users a single location to catalog and secure data in Hadoop and NoSQL systems, Oracle Database. Seamless metadata integration and queries which join data from Oracle Database with data from Hadoop and NoSQL databases. Utilities and conversion routines support automatic mappings from metadata stored in HCatalog (or the Hive Metastore) to Oracle Tables. Enhanced access parameters give administrators the flexibility to control column mapping and data access behavior. Multiple cluster support enables one Oracle Database to query multiple Hadoop clusters and/or NoSQL systems.
-
18
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. -
19
Apache Trafodion
Apache Software Foundation
Apache Trafodion is a webscale SQL-on-Hadoop solution enabling transactional or operational workloads on Apache Hadoop. Trafodion builds on the scalability, elasticity, and flexibility of Hadoop. Trafodion extends Hadoop to provide guaranteed transactional integrity, enabling new kinds of big data applications to run on Hadoop. Full-functioned ANSI SQL language support. JDBC/ODBC connectivity for Linux/Windows clients. Distributed ACID transaction protection across multiple statements, tables, and rows. Performance improvements for OLTP workloads with compile-time and run-time optimizations. Support for large data sets using a parallel-aware query optimizer. Reuse existing SQL skills and improve developer productivity. Distributed ACID transactions guarantee data consistency across multiple rows and tables. Interoperability with existing tools and applications. Hadoop and Linux distribution neutral. Easy to add to your existing Hadoop infrastructure.Starting Price: Free -
20
IBM Db2 Big SQL
IBM
A hybrid SQL-on-Hadoop engine delivering advanced, security-rich data query across enterprise big data sources, including Hadoop, object storage and data warehouses. IBM Db2 Big SQL is an enterprise-grade, hybrid ANSI-compliant SQL-on-Hadoop engine, delivering massively parallel processing (MPP) and advanced data query. Db2 Big SQL offers a single database connection or query for disparate sources such as Hadoop HDFS and WebHDFS, RDMS, NoSQL databases, and object stores. Benefit from low latency, high performance, data security, SQL compatibility, and federation capabilities to do ad hoc and complex queries. Db2 Big SQL is now available in 2 variations. It can be integrated with Cloudera Data Platform, or accessed as a cloud-native service on the IBM Cloud Pak® for Data platform. Access and analyze data and perform queries on batch and real-time data across sources, like Hadoop, object stores and data warehouses. -
21
Apache Ranger
The Apache Software Foundation
Apache Ranger™ is a framework to enable, monitor and manage comprehensive data security across the Hadoop platform. The vision with Ranger is to provide comprehensive security across the Apache Hadoop ecosystem. With the advent of Apache YARN, the Hadoop platform can now support a true data lake architecture. Enterprises can potentially run multiple workloads, in a multi tenant environment. Data security within Hadoop needs to evolve to support multiple use cases for data access, while also providing a framework for central administration of security policies and monitoring of user access. Centralized security administration to manage all security related tasks in a central UI or using REST APIs. Fine grained authorization to do a specific action and/or operation with Hadoop component/tool and managed through a central administration tool. Standardize authorization method across all Hadoop components. Enhanced support for different authorization methods - Role based access control etc. -
22
QuerySurge
RTTS
QuerySurge leverages AI to automate the data validation and ETL testing of Big Data, Data Warehouses, Business Intelligence Reports and Enterprise Apps/ERPs with full DevOps functionality for continuous testing. Use Cases - Data Warehouse & ETL Testing - Hadoop & NoSQL Testing - DevOps for Data / Continuous Testing - Data Migration Testing - BI Report Testing - Enterprise App/ERP Testing QuerySurge Features - Projects: Multi-project support - AI: automatically create datas validation tests based on data mappings - Smart Query Wizards: Create tests visually, without writing SQL - Data Quality at Speed: Automate the launch, execution, comparison & see results quickly - Test across 200+ platforms: Data Warehouses, Hadoop & NoSQL lakes, databases, flat files, XML, JSON, BI Reports - DevOps for Data & Continuous Testing: RESTful API with 60+ calls & integration with all mainstream solutions - Data Analytics & Data Intelligence: Analytics dashboard & reports -
23
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. -
24
Oracle Big Data Service
Oracle
Oracle Big Data Service makes it easy for customers to deploy Hadoop clusters of all sizes, with VM shapes ranging from 1 OCPU to a dedicated bare metal environment. Customers choose between high-performance NVmE storage or cost-effective block storage, and can grow or shrink their clusters. Quickly create Hadoop-based data lakes to extend or complement customer data warehouses, and ensure that all data is both accessible and managed cost-effectively. Query, visualize and transform data so data scientists can build machine learning models using the included notebook with its R, Python and SQL support. Move customer-managed Hadoop clusters to a fully-managed cloud-based service, reducing management costs and improving resource utilization.Starting Price: $0.1344 per hour -
25
Apache Storm
Apache Software Foundation
Apache Storm is a free and open source distributed realtime computation system. Apache Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Apache Storm is simple, can be used with any programming language, and is a lot of fun to use! Apache Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Apache Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate. Apache Storm integrates with the queueing and database technologies you already use. An Apache Storm topology consumes streams of data and processes those streams in arbitrarily complex ways, repartitioning the streams between each stage of the computation however needed. Read more in the tutorial. -
26
Apache Impala
Apache
Impala provides low latency and high concurrency for BI/analytic queries on the Hadoop ecosystem, including Iceberg, open data formats, and most cloud storage options. Impala also scales linearly, even in multitenant environments. Impala is integrated with native Hadoop security and Kerberos for authentication, and via the Ranger module, you can ensure that the right users and applications are authorized for the right data. Utilize the same file and data formats and metadata, security, and resource management frameworks as your Hadoop deployment, with no redundant infrastructure or data conversion/duplication. For Apache Hive users, Impala utilizes the same metadata and ODBC driver. Like Hive, Impala supports SQL, so you don't have to worry about reinventing the implementation wheel. With Impala, more users, whether using SQL queries or BI applications, can interact with more data through a single repository and metadata stored from source through analysis.Starting Price: Free -
27
Apache Bigtop
Apache Software Foundation
Bigtop is an Apache Foundation project for Infrastructure Engineers and Data Scientists looking for comprehensive packaging, testing, and configuration of the leading open source big data components. Bigtop supports a wide range of components/projects, including, but not limited to, Hadoop, HBase and Spark. Bigtop packages Hadoop RPMs and DEBs, so that you can manage and maintain your Hadoop cluster. Bigtop provides an integrated smoke testing framework, alongside a suite of over 50 test files. Bigtop provides vagrant recipes, raw images, and (work-in-progress) docker recipes for deploying Hadoop from zero. Bigtop support many Operating Systems, including Debian, Ubuntu, CentOS, Fedora, openSUSE and many others. Bigtop includes tools and a framework for testing at various levels (packaging, platform, runtime, etc.) for both initial deployments as well as upgrade scenarios for the entire data platform, not just the individual components. -
28
CONNX
Software AG
Unlock the value of your data—wherever it resides. To become data-driven, you need to leverage all the information in your enterprise across apps, clouds and systems. With the CONNX data integration solution, you can easily access, virtualize and move your data—wherever it is, however it’s structured—without changing your core systems. Get your information where it needs to be to better serve your organization, customers, partners and suppliers. Connect and transform legacy data sources from transactional databases to big data or data warehouses such as Hadoop®, AWS and Azure®. Or move legacy to the cloud for scalability, such as MySQL to Microsoft® Azure® SQL Database, SQL Server® to Amazon REDSHIFT®, or OpenVMS® Rdb to Teradata®. -
29
Load your data into or out of Hadoop and data lakes. Prep it so it's ready for reports, visualizations or advanced analytics – all inside the data lakes. And do it all yourself, quickly and easily. Makes it easy to access, transform and manage data stored in Hadoop or data lakes with a web-based interface that reduces training requirements. Built from the ground up to manage big data on Hadoop or in data lakes; not repurposed from existing IT-focused tools. Lets you group multiple directives to run simultaneously or one after the other. Schedule and automate directives using the exposed Public API. Enables you to share and secure directives. Call them from SAS Data Integration Studio, uniting technical and nontechnical user activities. Includes built-in directives – casing, gender and pattern analysis, field extraction, match-merge and cluster-survive. Profiling runs in-parallel on the Hadoop cluster for better performance.
-
30
Apache Knox
Apache Software Foundation
The Knox API Gateway is designed as a reverse proxy with consideration for pluggability in the areas of policy enforcement, through providers and the backend services for which it proxies requests. Policy enforcement ranges from authentication/federation, authorization, audit, dispatch, hostmapping and content rewrite rules. Policy is enforced through a chain of providers that are defined within the topology deployment descriptor for each Apache Hadoop cluster gated by Knox. The cluster definition is also defined within the topology deployment descriptor and provides the Knox Gateway with the layout of the cluster for purposes of routing and translation between user facing URLs and cluster internals. Each Apache Hadoop cluster that is protected by Knox has its set of REST APIs represented by a single cluster specific application context path. This allows the Knox Gateway to both protect multiple clusters and present the REST API consumer with a single endpoint. -
31
Apache Hive
Apache Software Foundation
The Apache Hive data warehouse software facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage. A command line tool and JDBC driver are provided to connect users to Hive. Apache Hive is an open source project run by volunteers at the Apache Software Foundation. Previously it was a subproject of Apache® Hadoop®, but has now graduated to become a top-level project of its own. We encourage you to learn about the project and contribute your expertise. Traditional SQL queries must be implemented in the MapReduce Java API to execute SQL applications and queries over distributed data. Hive provides the necessary SQL abstraction to integrate SQL-like queries (HiveQL) into the underlying Java without the need to implement queries in the low-level Java API. -
32
Apache Atlas
Apache Software Foundation
Atlas is a scalable and extensible set of core foundational governance services – enabling enterprises to effectively and efficiently meet their compliance requirements within Hadoop and allows integration with the whole enterprise data ecosystem. Apache Atlas provides open metadata management and governance capabilities for organizations to build a catalog of their data assets, classify and govern these assets and provide collaboration capabilities around these data assets for data scientists, analysts and the data governance team. Pre-defined types for various Hadoop and non-Hadoop metadata. Ability to define new types for the metadata to be managed. Types can have primitive attributes, complex attributes, object references; can inherit from other types. Instances of types, called entities, capture metadata object details and their relationships. REST APIs to work with types and instances allow easier integration. -
33
Yandex Data Proc
Yandex
You select the size of the cluster, node capacity, and a set of services, and Yandex Data Proc automatically creates and configures Spark and Hadoop clusters and other components. Collaborate by using Zeppelin notebooks and other web apps via a UI proxy. You get full control of your cluster with root permissions for each VM. Install your own applications and libraries on running clusters without having to restart them. Yandex Data Proc uses instance groups to automatically increase or decrease computing resources of compute subclusters based on CPU usage indicators. Data Proc allows you to create managed Hive clusters, which can reduce the probability of failures and losses caused by metadata unavailability. Save time on building ETL pipelines and pipelines for training and developing models, as well as describing other iterative tasks. The Data Proc operator is already built into Apache Airflow.Starting Price: $0.19 per hour -
34
Oracle Big Data Discovery
Oracle
Oracle Big Data Discovery is a stunningly visual, intuitive product that leverages the power of Hadoop to transform raw data into business insight in minutes, without the need to learn complex tools or rely only on highly specialized resources. With Oracle Big Data Discovery, customers can easily find relevant data sets in Hadoop, explore the data and quickly understand its potential, transform and enrich data to make it better, analyze the data to discover new insights, share results and publish back to Hadoop for use across the enterprise. In your organization, use BDD as the center of your data lab, as a unified environment for navigating and exploring all of your data sources in Hadoop, and to create projects and BDD applications. In BDD, a wider number of people can work with big data, compared with traditional analytics tools. You spend less time on data loading and updates, and can focus on actual data analysis of big data. -
35
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. -
36
Oracle Enterprise Metadata Management (OEMM) is a comprehensive metadata management platform. OEMM can harvest and catalog metadata from virtually any metadata provider, including relational, Hadoop, ETL, BI, data modeling, and many more. OEMM however is not just a metadata repository, OEMM allows for interactive searching and browsing of the metadata as well as providing data lineage, impact analysis, semantic definition and semantic usage analysis for any metadata asset within the catalog. OEMM's advanced algorithms stitch together metadata from each of the providers providing the complete path of data from source to report or vice versa. OEMM supports virtually any metadata provider including: Data modeling tools, databases, CASE tools, Hadoop, ETL engines, Warehouses, BI, EAI environments, as well as many more.
-
37
Adoki
Adastra
Adoki streamlines data transfers to and from any platform or system—whether it's a data warehouse, database, cloud service, Hadoop platform, or streaming application—on both one-time and recurring schedules. It adapts to your IT infrastructure's workload, adjusting transfer or replication processes to optimal times when needed. With centralized management and monitoring of data transfers, Adoki allows you to handle your data operations with a smaller, more efficient team. -
38
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. -
39
Apache Eagle
Apache Software Foundation
Apache Eagle (called Eagle in the following) is an open source analytics solution for identifying security and performance issues instantly on big data platforms, e.g. Apache Hadoop, Apache Spark etc. It analyzes data activities, yarn applications, jmx metrics, and daemon logs etc., provides state-of-the-art alert engine to identify security breach, performance issues and shows insights. Big data platform normally generates huge amount of operational logs and metrics in realtime. Eagle is founded to solve hard problems in securing and tuning performance for big data platforms by ensuring metrics, logs always available and alerting immediately even under huge traffic. Streaming operational logs and data activities into Eagle platform, including but not limited to audit logs, map/reduce jobs, yarn resource usage, jmx metrics and various daemon logs etc. Generate alerts, show historical trend, and correlate alert with raw data. -
40
Apache Accumulo
Apache Corporation
With Apache Accumulo, users can store and manage large data sets across a cluster. Accumulo uses Apache Hadoop's HDFS to store its data and Apache ZooKeeper for consensus. While many users interact directly with Accumulo, several open source projects use Accumulo as their underlying store. To learn more about Accumulo, take the Accumulo tour, read the user manual and run the Accumulo example code. Feel free to contact us if you have any questions. Accumulo has a programming mechanism (called Iterators) that can modify key/value pairs at various points in the data management process. Every Accumulo key/value pair has its own security label which limits query results based off user authorizations. Accumulo runs on a cluster using one or more HDFS instances. Nodes can be added or removed as the amount of data stored in Accumulo changes. -
41
Logi Symphony
insightsoftware
Fix data accuracy and alignment issues to give consumers a deeper understanding of their data. Implement a rich and highly customizable BI and analytics experience giving you the tools you need to create the complex dashboards and reports your users need. Partner with a company that takes a customer-centric approach to help your business achieve a lasting competitive advantage. Connect to any open data source from traditional databases, flat-file sources, Excel, or web-based data, through APIs. Embed advanced functionality like self-service, data discovery, and administration for external use. Visualize data using any chart type from a robust library of options or build unique visualizations using scorecards and small multiples. Connect to data stores such as cloud data warehouses, Hadoop, NoSQL document store, streaming, and search engine.Starting Price: $20 per month -
42
Apache HBase
The Apache Software Foundation
Use Apache HBase™ when you need random, realtime read/write access to your Big Data. This project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware. Automatic failover support between RegionServers. Easy to use Java API for client access. Thrift gateway and a REST-ful Web service that supports XML, Protobuf, and binary data encoding options. Support for exporting metrics via the Hadoop metrics subsystem to files or Ganglia; or via JMX. -
43
WANdisco
WANdisco
Since 2010 we have seen Hadoop become an essential part of the data management landscape. Over the decade the majority of organizations have adopted Hadoop to build out their data lake infrastructure. However, while Hadoop offered a cost-effective way to store petabytes of data across a distributed environment, it introduced many complexities. The systems required specialized IT skills and the on-premises environments lacked the flexibility to easily scale the systems up and down as usage demands changed. The management complexity and flexibility challenges associated with on-premises Hadoop environments are much more optimally addressed in the cloud. To minimize the risks and costs associated with these data modernization efforts, many companies have selected to automate their cloud data migration with WANdisco. LiveData Migrator is a fully self-service solution requiring no WANdisco expertise or services. -
44
Google Cloud Bigtable
Google
Google Cloud Bigtable is a fully managed, scalable NoSQL database service for large analytical and operational workloads. Fast and performant: Use Cloud Bigtable as the storage engine that grows with you from your first gigabyte to petabyte-scale for low-latency applications as well as high-throughput data processing and analytics. Seamless scaling and replication: Start with a single node per cluster, and seamlessly scale to hundreds of nodes dynamically supporting peak demand. Replication also adds high availability and workload isolation for live serving apps. Simple and integrated: Fully managed service that integrates easily with big data tools like Hadoop, Dataflow, and Dataproc. Plus, support for the open source HBase API standard makes it easy for development teams to get started. -
45
Trino
Trino
Trino is a query engine that runs at ludicrous speed. Fast-distributed SQL query engine for big data analytics that helps you explore your data universe. Trino is a highly parallel and distributed query engine, that is built from the ground up for efficient, low-latency analytics. The largest organizations in the world use Trino to query exabyte-scale data lakes and massive data warehouses alike. Supports diverse use cases, ad-hoc analytics at interactive speeds, massive multi-hour batch queries, and high-volume apps that perform sub-second queries. Trino is an ANSI SQL-compliant query engine, that works with BI tools such as R, Tableau, Power BI, Superset, and many others. You can natively query data in Hadoop, S3, Cassandra, MySQL, and many others, without the need for complex, slow, and error-prone processes for copying the data. Access data from multiple systems within a single query.Starting Price: Free -
46
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 -
47
HugeGraph
HugeGraph
HugeGraph is a fast-speed and highly-scalable graph database. Billions of vertices and edges can be easily stored into and queried from HugeGraph due to its excellent OLTP ability. As compliance to Apache TinkerPop 3 framework, various complicated graph queries can be accomplished through Gremlin (a powerful graph traversal language). Among its features, it provides compliance to Apache TinkerPop 3, supporting Gremlin. Schema Metadata Management, including VertexLabel, EdgeLabel, PropertyKey and IndexLabel. Multi-type Indexes, supporting exact query, range query and complex conditions combination query. Plug-in Backend Store Driver Framework, supporting RocksDB, Cassandra, ScyllaDB, HBase and MySQL now and easy to add other backend store driver if needed. Integration with Hadoop/Spark. HugeGraph relies on the TinkerPop framework, we refer to the storage structure of Titan and the schema definition of DataStax. -
48
Apache Giraph
Apache Software Foundation
Apache Giraph is an iterative graph processing system built for high scalability. For example, it is currently used at Facebook to analyze the social graph formed by users and their connections. Giraph originated as the open-source counterpart to Pregel, the graph processing architecture developed at Google and described in a 2010 paper. Both systems are inspired by the Bulk Synchronous Parallel model of distributed computation introduced by Leslie Valiant. Giraph adds several features beyond the basic Pregel model, including master computation, sharded aggregators, edge-oriented input, out-of-core computation, and more. With a steady development cycle and a growing community of users worldwide, Giraph is a natural choice for unleashing the potential of structured datasets at a massive scale. Apache Giraph is an iterative graph processing framework, built on top of Apache Hadoop. -
49
SAP BW/4HANA
SAP
SAP BW/4HANA is a packaged data warehouse based on SAP HANA. As the on-premise data warehouse layer of SAP’s Business Technology Platform, it allows you to consolidate data across the enterprise to get a consistent, agreed-upon view of your data. Streamline processes and support innovations with a single source for real-time insights. Based on SAP HANA, our next-generation data warehouse solution can help you capitalize on the full value of all your data from SAP applications or third-party solutions, as well as unstructured, geospatial, or Hadoop-based. Transform data practices to gain the efficiency and agility to deploy live insights at scale, both on premise or in the cloud. Drive digitization across all lines of business with a Big Data warehouse, while leveraging digital business platform solutions from SAP. -
50
Big Data Group
MAIA Intelligence
This is a vibrant community dedicated to promote Big Data & Visualization softwares, best practices and innovations needed for enterprises to get maximum value from massive amounts of data. 98000+ members Largest Big Data Experts Professional Group. Go beyond the big data hype. A premier community for both existing expert professionals and companies researching the convergence of big data analytics and discovery, Hadoop, data warehousing, cloud, unified data architectures, digital marketing, visualization and business intelligence. We hope to bring together stakeholder communities across industry, enterprises, academic, and government sectors representing all of those with interests in Big Data & Visualization techniques, technologies, and applications. The group needs your input to meet its goals so please join us for the discussion, expert comments, learning's and contribute your ideas and insights.
