Alternatives to Azure HDInsight

Google Cloud is an online service that lets you create everything from simple websites to complex apps for businesses of any size. Customers who are new to the system will receive $300 in credits for testing, deploying, and running workloads. Customers can use up to 25+ products free of charge. Use Google's core data analytics and machine learning. All enterprises can use it. It is secure and fully featured. Use big data to build better products and find answers faster. You can grow from prototypes to production and even to planet-scale without worrying about reliability, capacity or performance. Virtual machines with proven performance/price advantages, to a fully-managed app development platform. High performance, scalable, resilient object storage and databases. Google's private fibre network offers the latest software-defined networking solutions. Fully managed data warehousing and data exploration, Hadoop/Spark and messaging.

With the platform that delivers sub-second response at scale, you can quickly deploy consumer-grade BI experiences on any device for every role. In minutes, create consumer-grade intelligence apps, empower users with data discovery, then seamlessly push content to customers, partners, employees, and employees. Our open platform allows you to inject the data that you trust into the tools that you love using our platform. MicroStrategy's #1-rated platform to embed embedded analytics. Mobile intelligence solutions can be deployed for any user on any device. They can be customized for your company without any coding. This is the fastest and most efficient way to run an Intelligent Enterprise.

IRI Voracity is an end-to-end software platform for fast, affordable, and ergonomic data lifecycle management. Voracity speeds, consolidates, and often combines the key activities of data discovery, integration, migration, governance, and analytics in a single pane of glass, built on Eclipse™. Through its revolutionary convergence of capability and its wide range of job design and runtime options, Voracity bends the multi-tool cost, difficulty, and risk curves away from megavendor ETL packages, disjointed Apache projects, and specialized software. Voracity uniquely delivers the ability to perform data: * profiling and classification * searching and risk-scoring * integration and federation * migration and replication * cleansing and enrichment * validation and unification * masking and encryption * reporting and wrangling * subsetting and testing Voracity runs on-premise, or in the cloud, on physical or virtual machines, and its runtimes can also be containerized or called from real-time applications or batch jobs.

Gartner's Magic Quadrant includes Centralpoint as a Digital Experience Platform. It is used by more than 350 clients around the world, and it goes beyond Enterprise Content Management. It securely authenticates (AD/SAML/OpenID, oAuth), all users for self-service interaction. Centralpoint automatically aggregates information from different sources and applies rich metadata against your rules to produce true Knowledge Management. This allows you to search for and relate disparate data sets from anywhere. Centralpoint's Module Gallery is the most robust and can be installed either on-premise or in the cloud. Check out our solutions for Automating Metadata and Automating Retention Policy Management. We also offer solutions to simplify the mashup of disparate data to benefit from AI (Artificial Intelligence). Centralpoint is often used to provide easy migration tools and an intelligent alternative to Sharepoint. It can be used to secure portal solutions for public sites, intranets, members, or extranets.

EMR is an enterprise-ready big-data platform that offers cluster, job, data management and other services. It is based on open-source ecosystems such as Hadoop Spark, Kafka and Flink. Alibaba Cloud Elastic MapReduce is a big-data processing solution that runs on the Alibaba Cloud platform. EMR is built on Alibaba Cloud ECS and is based open-source Apache Spark and Apache Hadoop. EMR allows you use the Hadoop/Spark ecosystem components such as Apache Hive and Apache Kafka, Flink and Druid to analyze and process data. EMR can be used to process data stored on different Alibaba Cloud data storage services, such as Log Service (SLS), Object Storage Service(OSS), and Relational Data Service (RDS). It is easy to create clusters quickly without having to install hardware or software. Its Web interface allows you to perform all maintenance operations.

Data integration for hybrid clouds Modern, reliable data integration across both your private cloud and public cloud. All this in real-time, with change data capture and streams. Striim was developed by the executive and technical team at GoldenGate Software. They have decades of experience in mission critical enterprise workloads. Striim can be deployed in your environment as a distributed platform or in the cloud. Your team can easily adjust the scaleability of Striim. Striim is fully secured with HIPAA compliance and GDPR compliance. Built from the ground up to support modern enterprise workloads, whether they are hosted in the cloud or on-premise. Drag and drop to create data flows among your sources and targets. Real-time SQL queries allow you to process, enrich, and analyze streaming data.

Azure Databricks allows you to unlock insights from all your data, build artificial intelligence (AI), solutions, and autoscale your Apache Spark™. You can also collaborate on shared projects with other people in an interactive workspace. Azure Databricks supports Python and Scala, R and Java, as well data science frameworks such as TensorFlow, PyTorch and scikit-learn. Azure Databricks offers the latest version of Apache Spark and allows seamless integration with open-source libraries. You can quickly spin up clusters and build in an Apache Spark environment that is fully managed and available worldwide. Clusters can be set up, configured, fine-tuned, and monitored to ensure performance and reliability. To reduce total cost of ownership (TCO), take advantage of autoscaling or auto-termination.

Amazon EMR is the market-leading cloud big data platform. It processes large amounts of data with open source tools like Apache Spark, Apache Hive and Apache HBase. EMR allows you to run petabyte-scale analysis at a fraction of the cost of traditional on premises solutions. It is also 3x faster than standard Apache Spark. You can spin up and down clusters for short-running jobs and only pay per second for the instances. You can also create highly available clusters that scale automatically to meet the demand for long-running workloads. You can also run EMR clusters from AWS Outposts if you have on-premises open source tools like Apache Spark or Apache Hive.

Apache Spark™, a unified analytics engine that can handle large-scale data processing, is available. Apache Spark delivers high performance for streaming and batch data. It uses a state of the art DAG scheduler, query optimizer, as well as a physical execution engine. Spark has over 80 high-level operators, making it easy to create parallel apps. You can also use it interactively via the Scala, Python and R SQL shells. Spark powers a number of libraries, including SQL and DataFrames and MLlib for machine-learning, GraphX and Spark Streaming. These libraries can be combined seamlessly in one application. Spark can run on Hadoop, Apache Mesos and Kubernetes. It can also be used standalone or in the cloud. It can access a variety of data sources. Spark can be run in standalone cluster mode on EC2, Hadoop YARN and Mesos. Access data in HDFS and Alluxio.

Dataproc makes it easy to process open source data and analytic processing in the cloud. Faster build custom OSS clusters for custom machines Dataproc can speed up your data and analytics processing, whether you need more memory for Presto or GPUs to run Apache Spark machine learning. It spins up a cluster in less than 90 seconds. Cluster management is easy and affordable Dataproc offers autoscaling, idle cluster deletion and per-second pricing. This allows you to focus your time and resources on other areas. Security built in by default Encryption by default ensures that no data is left unprotected. Component Gateway and JobsAPI allow you to define permissions for Cloud IAM clusters without the need to set up gateway or networking nodes.

Delta Lake is an open-source storage platform that allows ACID transactions to Apache Spark™, and other big data workloads. Data lakes often have multiple data pipelines that read and write data simultaneously. This makes it difficult for data engineers to ensure data integrity due to the absence of transactions. Your data lakes will benefit from ACID transactions with Delta Lake. It offers serializability, which is the highest level of isolation. Learn more at Diving into Delta Lake - Unpacking the Transaction log. Even metadata can be considered "big data" in big data. Delta Lake treats metadata the same as data and uses Spark's distributed processing power for all its metadata. Delta Lake is able to handle large tables with billions upon billions of files and partitions at a petabyte scale. Delta Lake allows developers to access snapshots of data, allowing them to revert to earlier versions for audits, rollbacks, or to reproduce experiments.

A hybrid SQL-onHadoop engine that delivers advanced, security-rich data queries across enterprise big data sources including Hadoop object storage and data warehouses. IBM Db2 Big SQL, an enterprise-grade, hybrid ANSI compliant SQL-on-Hadoop engine that delivers massively parallel processing and advanced data query, is available. Db2 Big SQL allows you to connect to multiple sources, such as Hadoop HDFS and WebHDFS. RDMS, NoSQL database, object stores, and RDMS. You can benefit from low latency, high speed, data security, SQL compatibility and federation capabilities to perform complex and ad-hoc queries. Db2 Big SQL now comes in two versions. It can be integrated with Cloudera Data Platform or accessed as a cloud native service on the IBM Cloud Pak®. for Data platform. Access, analyze, and perform queries on real-time and batch data from multiple sources, including Hadoop, object stores, and data warehouses.

All your data, analytics, and AI in one unified platform. Databricks is powered by Delta Lake. It combines the best data warehouses with data lakes to create a lakehouse architecture that allows you to collaborate on all your data, analytics, and AI workloads. We are the original developers of Apache Spark™, Delta Lake, and MLflow. We believe open source software is the key to the future of data and AI. Your business can be built on an open, cloud-agnostic platform. Databricks supports customers all over the world on AWS, Microsoft Azure, or Alibaba cloud. Our platform integrates tightly with the cloud providers' security, compute storage, analytics and AI services to help you unify your data and AI workloads.

Arcadia Data is the first native Hadoop and cloud (big) visual analytics and BI platform that provides the scale, performance and agility business users require for real-time and historical insight. Arcadia Enterprise, its flagship product, was created from the beginning for big data platforms like Apache Hadoop, Apache Spark and Apache Kafka. It can be used on-premises or in the cloud. Arcadia Enterprise uses artificial intelligence (AI), machine learning (ML) to streamline the self-service analytics process. It offers search-based BI, visualization recommendations, and a streamlined self-service analytics process. It provides real-time, high definition insights in use cases such as data lakes, cybersecurity, and customer intelligence. Some of the most recognizable brands in the world use Arcadia Enterprise, including Procter & Gamble and Nokia, Procter & Gamble and Citibank, Nokia, Royal Bank of Canada. Kaiser Permanente, HPE and Neustar.

Doolytic is a leader in big data discovery, the convergence data discovery, advanced analytics and big data. Doolytic is bringing together BI experts to revolutionize self-service exploration of large data. This will unleash the data scientist in everyone. doolytic is an enterprise solution for native big data discovery. doolytic is built on open-source, scalable technologies that are best-of-breed. Lightening performance on billions and petabytes. Structured, unstructured, and real-time data from all sources. Advanced query capabilities for experts, Integration with R to enable advanced and predictive applications. With Elastic's flexibility, you can search, analyze, and visualize data in real-time from any format or source. You can harness the power of Hadoop data lakes without any latency or concurrency issues. doolytic solves common BI issues and enables big data discovery without clumsy or inefficient workarounds.

StarTree Cloud is a fully-managed user-facing real-time analytics Database-as-a-Service (DBaaS) designed for OLAP at massive speed and scale. Powered by Apache Pinot, StarTree Cloud provides enterprise-grade reliability and advanced capabilities such as tiered storage, plus additional indexes and connectors. It integrates seamlessly with transactional databases and event streaming platforms, ingesting data at millions of events per second and indexing it for lightning-fast query responses. StarTree Cloud is available on your favorite public cloud or for private SaaS deployment. StarTree Cloud includes StarTree Data Manager, which allows you to ingest data from both real-time sources such as Amazon Kinesis, Apache Kafka, Apache Pulsar, or Redpanda, as well as batch data sources such as data warehouses like Snowflake, Delta Lake or Google BigQuery, or object stores like Amazon S3, Apache Flink, Apache Hadoop, or Apache Spark. StarTree ThirdEye is an add-on anomaly detection system running on top of StarTree Cloud that observes your business-critical metrics, alerting you and allowing you to perform root-cause analysis — all in real-time.

GeoSpock DB - The space-time analytics database - allows data fusion in the connected world. GeoSpockDB is a unique cloud-native database that can be used to query for real-world applications. It can combine multiple sources of Internet of Things data to unlock their full potential, while simultaneously reducing complexity, cost, and complexity. GeoSpock DB enables data fusion and efficient storage. It also allows you to run ANSI SQL query and connect to analytics tools using JDBC/ODBC connectors. Users can perform analysis and share insights with familiar toolsets. This includes support for common BI tools such as Tableau™, Amazon QuickSight™, and Microsoft Power BI™, as well as Data Science and Machine Learning environments (including Python Notebooks or Apache Spark). The database can be integrated with internal applications as well as web services, including compatibility with open-source visualisation libraries like Cesium.js and Kepler.

What is Presto? Presto is a distributed, federated SQL query engine that runs on multiple machines. It allows interactive, ad-hoc analysis on large quantities of data. You can run Presto SQL queries against your data. PrestoDB allows you to query data wherever it is located, including Hive and AWS S3, Hadoop and Cassandra, as well as relational databases, NoSQL database, or proprietary data stores. Presto is an open-source engine that allows users to access multiple sources of data, allowing them to perform analytics across the entire organization. A complete Presto installation requires a coordinator and multiple workers. Queries are sent from clients such as the Presto client to the coordinator. The coordinator analyzes, plans, and executes Presto queries.

Data-driven decision making has led to a surge in business data and an increase in demand for its analysis. IT departments are now looking to move away from expensive Enterprise Data Warehouses (EDW), and towards more cost-effective Big Data platforms such as Hadoop or AWS. The Total Cost of Ownership (TCO), for these new platforms, is approximately 10 times lower. They are not suitable for interactive BI applications as they lack the same performance and user concurrency as legacy EDWs. Jethro was created precisely for this purpose. Customers use Jethro to perform interactive BI with Big Data. Jethro is a transparent middle-tier that does not require any changes to existing apps and data. It is self-driving and requires no maintenance. Jethro is compatible to BI tools such as Microstrategy, Qlik and Tableau and is data source agnostic. Jethro meets the needs of business users by allowing thousands of concurrent users to run complex queries across billions of records.

Hopsworks is an open source Enterprise platform that allows you to develop and operate Machine Learning (ML), pipelines at scale. It is built around the first Feature Store for ML in the industry. You can quickly move from data exploration and model building in Python with Jupyter notebooks. Conda is all you need to run production-quality end-to-end ML pipes. Hopsworks can access data from any datasources you choose. They can be in the cloud, on premise, IoT networks or from your Industry 4.0-solution. You can deploy on-premises using your hardware or your preferred cloud provider. Hopsworks will offer the same user experience in cloud deployments or the most secure air-gapped deployments.

Starburst allows you to make better decisions by having quick access to all of your data. Your company has more data than ever, but your data teams are still waiting to analyze it. Starburst gives your data teams quick and accurate access to more data. Starburst Enterprise, a fully supported, production-tested, enterprise-grade distribution for open source Trino (formerly Presto®, SQL), is now available. It increases performance and security, while making it easy for you to deploy, connect, manage, and manage your Trino environment. Starburst allows your team to connect to any source of data, whether it's on-premise, in a cloud, or across a hybrid cloud environment. This allows them to use the analytics tools they already love and access data that lives anywhere.

Apache Druid, an open-source distributed data store, is Apache Druid. Druid's core design blends ideas from data warehouses and timeseries databases to create a high-performance real-time analytics database that can be used for a wide range of purposes. Druid combines key characteristics from each of these systems into its ingestion, storage format, querying, and core architecture. Druid compresses and stores each column separately, so it only needs to read the ones that are needed for a specific query. This allows for fast scans, ranking, groupBys, and groupBys. Druid creates indexes that are inverted for string values to allow for fast search and filter. Connectors out-of-the box for Apache Kafka and HDFS, AWS S3, stream processors, and many more. Druid intelligently divides data based upon time. Time-based queries are much faster than traditional databases. Druid automatically balances servers as you add or remove servers. Fault-tolerant architecture allows for server failures to be avoided.

Apache Storm is an open-source distributed realtime computing system that is free and open-source. Apache Storm makes it simple to process unbounded streams and data reliably, much like Hadoop did for batch processing. Apache Storm is easy to use with any programming language and is a lot fun! Apache Storm can be used for many purposes: realtime analytics and online machine learning. It can also be used with any programming language. Apache Storm is fast. A benchmark measured it at more than a million tuples per second per node. It is highly scalable, fault-tolerant and guarantees that your data will be processed. It is also easy to set up. Apache Storm can be integrated with the queueing and databases technologies you already use. Apache Storm topology processes streams of data in arbitrarily complex ways. It also partitions the streams between each stage of the computation as needed. Learn more in the tutorial.

EspressRepot ES (Enterprise Server), a web- and desktop-based software, allows users to create stunning interactive data visualizations and reports. The platform supports Java EE integration to draw data from data sources like Bid Data (Hadoop Spark and MongoDB), ad hoc queries and reports as well as online map support, mobile compatibility and alert monitor.

OctoData can be deployed in Cloud hosting at a lower price and includes personalized support, from the initial definition of your needs to the actual use of the solution. OctoData is built on open-source technologies that are innovative and can adapt to new possibilities. Its Supervisor provides a management interface that allows users to quickly capture, store, and exploit increasing amounts and varieties of data. OctoData allows you to quickly prototype and industrialize massive data recovery solutions, even in real-time, in a single environment. You can get precise reports, explore new options, increase productivity, and increase profitability by leveraging your data.

Oracle Cloud Infrastructure (OCI Data Flow) is a fully managed Apache Spark service that performs processing tasks on very large data sets. There is no infrastructure to deploy or manage. This allows developers to focus on application development and not infrastructure management, allowing for rapid application delivery. OCI Data Flow manages infrastructure provisioning, network setup, teardown, and completion of Spark jobs. Spark applications for big data analysis are easier to create and manage because storage and security are managed. OCI Data Flow does not require clusters to be installed, patched, or upgraded, which reduces both time and operational costs. OCI Data Flow runs every Spark job in dedicated resources. This eliminates the need to plan for capacity ahead. OCI Data Flow allows IT to only pay for the infrastructure resources used by Spark jobs while they are running.

You can easily develop and execute massively parallel data processing and transformation programs in U-SQL and R. You don't need to maintain any infrastructure and can process data on-demand, scale instantly, or pay per job. Azure Data Lake Analytics makes it easy to process large data jobs in seconds. There are no servers, virtual machines or clusters to manage or tune. You can instantly scale your processing power in Azure Data Lake Analytics Units, (AU), to one to thousands per job. Only pay for the processing you use per job. Optimized data virtualization of relational sources, such as Azure SQL Database or Azure Synapse Analytics, allows you to access all your data. Your queries are automatically optimized by moving processing closer to the source data, which maximizes performance while minimising latency.

Apache Hadoop is a software library that allows distributed processing of large data sets across multiple computers. It uses simple programming models. It can scale from one server to thousands of machines and offer local computations and storage. Instead of relying on hardware to provide high-availability, it is designed to detect and manage failures at the application layer. This allows for highly-available services on top of a cluster computers that may be susceptible to failures.

Keen is a fully managed event streaming platform. Our real-time data pipeline, built on Apache Kafka, makes it easy to collect large amounts of event data. Keen's powerful REST APIs and SDKs allow you to collect event data from any device connected to the internet. Our platform makes it possible to securely store your data, reducing operational and delivery risks with Keen. Apache Cassandra's storage infrastructure ensures data is completely secure by transferring it via HTTPS and TLS. The data is then stored with multilayer AES encryption. Access Keys allow you to present data in an arbitrary way without having to re-architect or re-architect the data model. Role-based Access Control allows for completely customizable permission levels, down to specific queries or data points.

QuerySurge is the smart Data Testing solution that automates the data validation and ETL testing of Big Data, Data Warehouses, Business Intelligence Reports and Enterprise Applications with full DevOps functionality for continuous testing. Use Cases - Data Warehouse & ETL Testing - Big Data (Hadoop & NoSQL) Testing - DevOps for Data / Continuous Testing - Data Migration Testing - BI Report Testing - Enterprise Application/ERP Testing Features Supported Technologies - 200+ data stores are supported QuerySurge Projects - multi-project support Data Analytics Dashboard - provides insight into your data Query Wizard - no programming required Design Library - take total control of your custom test desig BI Tester - automated business report testing Scheduling - run now, periodically or at a set time Run Dashboard - analyze test runs in real-time Reports - 100s of reports API - full RESTful API DevOps for Data - integrates into your CI/CD pipeline Test Management Integration QuerySurge will help you: - Continuously detect data issues in the delivery pipeline - Dramatically increase data validation coverage - Leverage analytics to optimize your critical data - Improve your data quality at speed

Access granular data to make it easy to slice and dice billions of rows. Real-time reporting is possible in just seconds. Sigmoid's Sigview real-time data analytics tool is a plug-and-play solution that allows for exploratory data analysis. Sigview, which is built on Apache Spark, can drill down into large data sets in a matter of seconds. Around 30k people use Sigview to analyze billions in ad impressions. Sigview allows for real-time access both to programmatic and non-programmatic data. It creates real-time reports and analyses large data sets to provide real-time insight. Sigview is the best platform to help you optimize your ad campaigns, discover new inventory, or generate revenue opportunities in changing times. Connects to multiple data sources such as DFP, Pixel Servers and Audience, allowing you to ingest data from any format and location, with a data latency of less that 15 minutes.

EMR allows you to scale managed Hadoop clusters manually, or automatically, according to your monitoring metrics or business curves. EMR's storage computation separation allows you to terminate clusters to maximize resource efficiency. EMR supports hot failover on CBS-based nodes. It has a primary/secondary disaster recovery mechanism that allows the secondary node to start within seconds of the primary node failing, ensuring high availability of big data services. Remote disaster recovery is possible because of the metadata in Hive's components. High data persistence is possible with computation-storage separation for COS data storage. EMR comes with a comprehensive monitoring system that allows you to quickly locate and identify cluster exceptions in order to ensure stable cluster operations. VPCs are a convenient network isolation method that allows you to plan your network policies for managed Hadoop clusters.

Kyligence Zen can collect, organize, and analyze your metrics, so you can spend more time taking action. Kyligence Zen, the low-code metrics platform, is the best way to define, collect and analyze your business metrics. It allows users to connect their data sources quickly, define their business metrics in minutes, uncover hidden insights, and share these across their organization. Kyligence Enterprise offers a variety of solutions based on public cloud, on-premises, and private cloud. This allows enterprises of all sizes to simplify multidimensional analyses based on massive data sets according to their needs. Kyligence Enterprise based on Apache Kylin provides sub-second standard SQL queries based upon PB-scale datasets. This simplifies multidimensional data analysis for enterprises, allowing them to quickly discover the business value of massive amounts data and make better business decisions.

Hydrolix is a streaming lake of data that combines decoupled archiving, indexed searching, and stream processing for real-time query performance on terabyte scale at a dramatically lower cost. CFOs love that data retention costs are 4x lower. Product teams appreciate having 4x more data at their disposal. Scale up resources when needed and down when not. Control costs by fine-tuning resource consumption and performance based on workload. Imagine what you could build if you didn't have budget constraints. Log data from Kafka, Kinesis and HTTP can be ingested, enhanced and transformed. No matter how large your data, you will only get the data that you need. Reduce latency, costs, and eliminate timeouts and brute-force queries. Storage is decoupled with ingest and queries, allowing them to scale independently to meet performance and cost targets. Hydrolix's HDX (high-density compress) reduces 1TB to 55GB.

Four solutions, one platform INDICA connects with all company applications and data sources. It indexes all data and gives you a complete view of your data landscape. INDICA provides four solutions using its platform. INDICA Enterprise Search allows access to all corporate data sources via one interface. It indexes both structured and unstructured data, and ranks the results according to their relevance. INDICA eDiscovery is available as a case-by-case platform or as a platform that allows you to conduct fraud and compliance investigations on the spot. The INDICA Privacy suite provides a comprehensive toolkit that will allow you to ensure your organization is compliant with GDPR and CCPA laws. INDICA Data Lifecycle Management lets you take control of your corporate data. You can keep track of your data, clean up or migrate your data. INDICA's data platform offers a wide range of features that will allow you to take control of your data.

ProjectPro is the only platform that can create ready-made, complete projects. We offer ready-made AI/ML/Big Data/Cloud templates that solve real-world business problems. Developers can speed up their work and learn from real-world use cases. ProjectPro provides end-to-end, ready-to-deploy, enterprise-grade big data, and data science projects for reuse and upskilling. Each project addresses a real business problem and includes solution code, explanation videos and cloud lab. Tech support is also available. Stop searching multiple online forums for solutions. You can get ready-made solutions for your project from data extraction, analysis, visualization, and deployment. Our investors include Sequoia Capital, the first investor in Apple, Google YouTube, YouTube etc., and YCombinator, which includes investors in Stripe, Dropbox, Airbnb etc. Why Project Pro - End to end implementation - Real industry-grade projects by industry experts - Ready-made solutions

Instaclustr, the Open Source-as a Service company, delivers reliability at scale. We provide database, search, messaging, and analytics in an automated, trusted, and proven managed environment. We help companies focus their internal development and operational resources on creating cutting-edge customer-facing applications. Instaclustr is a cloud provider that works with AWS, Heroku Azure, IBM Cloud Platform, Azure, IBM Cloud and Google Cloud Platform. The company is certified by SOC 2 and offers 24/7 customer support.

Today there is a constant buzz about how top companies are using big data to gain competitive advantage. Your company is trying to be one of these market-leading companies. The reality is that more than 80% of big-data projects fail to go to production. This is because project implementation can be complex and resource-intensive. It can take months, if not years, to complete. The technology is complex and the people with the right skills are difficult to find. Automates all data workflows, from source to consumption. Automates the migration of data and workloads between legacy Data Warehouse systems and big data platforms. Automates the orchestration and management complex data pipelines in production. Alternative methods, such as custom development or stitching together multiple points solutions, are more expensive, inflexible and time-consuming, and require specialized skills to assemble.

Qubole is an open, secure, and simple Data Lake Platform that enables machine learning, streaming, or ad-hoc analysis. Our platform offers end-to-end services to reduce the time and effort needed to run Data pipelines and Streaming Analytics workloads on any cloud. Qubole is the only platform that offers more flexibility and openness for data workloads, while also lowering cloud data lake costs up to 50%. Qubole provides faster access to trusted, secure and reliable datasets of structured and unstructured data. This is useful for Machine Learning and Analytics. Users can efficiently perform ETL, analytics, or AI/ML workloads in an end-to-end fashion using best-of-breed engines, multiple formats and libraries, as well as languages that are adapted to data volume and variety, SLAs, and organizational policies.

Multi-cloud data security with a single pane of glass Industry's first SaaS access governance solution. Cloud is fragmented and data is scattered across different systems. Sensitive data is difficult to access and control due to limited visibility. Complex data onboarding hinders data scientist productivity. Data governance across services can be manual and fragmented. It can be time-consuming to securely move data to the cloud. Maximize visibility and assess the risk of sensitive data distributed across multiple cloud service providers. One system that enables you to manage multiple cloud services' data policies in a single place. Support RTBF, GDPR and other compliance requests across multiple cloud service providers. Securely move data to the cloud and enable Apache Ranger compliance policies. It is easier and quicker to transform sensitive data across multiple cloud databases and analytical platforms using one integrated system.

Customers can deploy Hadoop clusters in any size using Oracle Big Data Service. VM shapes range from 1 OCPU up to a dedicated bare-metal environment. Customers can choose between high-performance block storage or cost-effective block store, and can grow and shrink their clusters. Create Hadoop-based data lakes quickly to expand or complement customer data warehouses and ensure that all data can be accessed and managed efficiently. The included notebook supports R, Python, and SQL. Data scientists can query, visualize, and transform data to build machine learning models. Transfer customer-managed Hadoop clusters from a managed cloud-based service to improve resource utilization and reduce management costs.

Secure and manage the data lifecycle, from Edge to AI in any cloud or data centre. Operates on all major public clouds as well as the private cloud with a public experience everywhere. Integrates data management and analytics experiences across the entire data lifecycle. All environments are covered by security, compliance, migration, metadata management. Open source, extensible, and open to multiple data stores. Self-service analytics that is faster, safer, and easier to use. Self-service access to multi-function, integrated analytics on centrally managed business data. This allows for consistent experiences anywhere, whether it is in the cloud or hybrid. You can enjoy consistent data security, governance and lineage as well as deploying the cloud analytics services that business users need. This eliminates the need for shadow IT solutions.

You can share data with other organizations in any format and size. You can easily control what data you share, who gets it, and the terms of your use. Data Share gives you full visibility into all data-sharing relationships through a user-friendly interface. You can share data with just a few clicks or create your own application using REST API. Serverless code-free data sharing service that doesn't require infrastructure setup or management. An intuitive interface to manage all data-sharing relationships. Automated data sharing for predictability and productivity. Secure data-sharing service that utilizes underlying Azure security measures. In just a few clicks, you can share structured and unstructured data from multiple Azure storages with other organizations. There is no infrastructure to create or manage, no SAS keys required, and sharing data is completely code-free. You can control data access and set terms that are consistent with your enterprise policies.

Apache Arrow is a language-independent columnar storage format for flat and hierarchical data. It's designed for efficient analytic operations with modern hardware such as CPUs and GPUs. The Arrow memory format supports zero-copy reads, which allows for lightning-fast data access with no serialization overhead. Arrow's libraries support the format and can be used to build blocks for a variety of applications, including high-performance analytics. Arrow is used by many popular projects to efficiently ship columnar data or as the basis of analytic engines. Apache Arrow is software that was created by and for developers. We believe in open, honest communication and consensus decisionmaking. We welcome all to join us. Our committers come in a variety of backgrounds and organizations.

ROBIN is the first hyper-converged Kubernetes platform in the industry for big data, databases and AI/ML. The platform offers a self-service App store experience to deploy any application anywhere. It runs on-premises in your private cloud or in public-cloud environments (AWS, Azure and GCP). Hyper-converged Kubernetes combines containerized storage and networking with compute (Kubernetes) and the application management layer to create a single system. Our approach extends Kubernetes to data-intensive applications like Hortonworks, Cloudera and Elastic stack, RDBMSs, NoSQL database, and AI/ML. Facilitates faster and easier roll-out of important Enterprise IT and LoB initiatives such as containerization and cloud-migration, cost consolidation, productivity improvement, and cost-consolidation. This solution addresses the fundamental problems of managing big data and databases in Kubernetes.

Bi(OS)®, a single platform that provides unparalleled speed and insight for data app developers, enables them to build apps in a more unified way. The entire life-cycle of building data applications takes just hours to complete with bi(OS®. This includes adding diverse data sources, generating real-time insights and deploying to production. Join enterprise data teams from across industries to become the data superhero that your business needs. The promised data-driven impact of Open Source, Cloud, or SaaS has not been realized by the trio of Open Source, Cloud, or SaaS. All the investments made by enterprises have been in data integration and movement, which is not sustainable. A new approach to data is needed that is enterprise-focused. Bi(OS)®, is a reimagining of the first principles of enterprise data management, from ingest through insight. It supports API, AI, BI builders and other unified functions to deliver data-driven impact in days. Engineers create an enduring moat when a symphony between IT teams, tools and processes emerges.

Integrate analytics into any workflow or application to make crucial decisions - confidently. Analytics can be integrated into your everyday workflows and applications to help you make better and faster decisions for your business and customers. To make analytics easy and intuitive, integrate customized analytics into your products and applications. The AI-driven predictive analytics platform is designed to increase product adoption, retention, and engagement. Sisense, a top-rated Business Intelligence (BI), reporting software, allows you to prepare, analyze, and examine data from multiple sources. Sisense is trusted by industry-leading companies like NASDAQ, Phillips and Airbus. It offers an end to end, agile BI platform that enables businesses to make better, faster data-driven business decisions. Sisense has an open, single-stack architecture that enables machine learning, best-in class analytics engines, and delivers insights beyond the dashboard.

Unlike most data management systems, PHEMI Health DataLab is built with Privacy-by-Design principles, not as an add-on. This means privacy and data governance are built-in from the ground up, providing you with distinct advantages: Lets analysts work with data without breaching privacy guidelines Includes a comprehensive, extensible library of de-identification algorithms to hide, mask, truncate, group, and anonymize data. Creates dataset-specific or system-wide pseudonyms enabling linking and sharing of data without risking data leakage. Collects audit logs concerning not only what changes were made to the PHEMI system, but also data access patterns. Automatically generates human and machine-readable de- identification reports to meet your enterprise governance risk and compliance guidelines. Rather than a policy per data access point, PHEMI gives you the advantage of one central policy for all access patterns, whether Spark, ODBC, REST, export, and more

The Iguazio MLOps Platform turns AI projects into real-world business results. You can accelerate and scale the development, deployment, and management of your AI apps with end-to–end automation of deep and machine learning pipelines. A fully integrated platform allows you to seamlessly deploy machine and deep learning models to high-powered business applications, reducing time to market and achieving real-time enterprise performance. Continuously and seamlessly deploy new model into business environments, monitor models during production, detect and mitigate drift, save time and money on operationalizing machine-learning, and save time. Automate and accelerate data science workflows so concepts flow smoothly from development through deployment to impact. Monitor Models, Detect Drift, and Auto-Trigger Training. You can deploy with ease to an Operational Pipeline.

IBM Analytics Engine is an architecture for Hadoop clusters that separates the compute and storage layers. Instead of a permanent cluster of dual-purpose nodes the Analytics Engine allows users store data in an object storage layer like IBM Cloud Object Storage. It also spins up clusters with computing notes as needed. The flexibility, scalability, and maintainability of big-data analytics platforms can be improved by separating compute from storage. With the Apache Hadoop and Apache Spark ecosystems, you can build an ODPi-compliant stack that includes cutting-edge data science tools. Define clusters according to your application's needs. Select the appropriate software pack, version, size, and type of cluster. You can use the cluster for as long as you need and then delete it as soon as the job is finished. Create clusters using third-party packages and analytics libraries. Use IBM Cloud services to deploy workloads such as machine learning.