Alternatives to Azure Databricks

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.

Azure Data Explorer provides fast, fully managed data analytics services for real-time analysis of large amounts of data streaming from websites, applications, IoT devices, etc. Ask questions and iteratively analyze data on the fly to improve products and customer experiences, monitor devices, boost operations, and increase profits. Identify patterns, anomalies, or trends quickly in your data. Find answers to your questions quickly and easily by exploring new topics. The optimized cost structure allows you to run as many queries as needed. You can explore new possibilities with your data efficiently. With the fully managed, easy-to-use data analytics service, you can focus on insights and not infrastructure. Rapidly respond to rapidly changing and fast-flowing data. Azure Data Explorer simplifies analytics for all types of streaming data.

Business Process Management Software (BPMS & BPM Workflow Automation Software). Companies require support for workflow, forms, compliance, as well as automation routing. Low-code options make it easy to create and edit workflows. Our best-in class forms capabilities make it easy to quickly build forms, logic, and workflows for forms driven workflow processes. Many systems are already in place and need to be integrated. Our business process integrations between systems are loosely-coupled and intelligently integrated. FlowWright allows you to access standard metrics as well as those you create when automating your business. BPM analytics are an integral part of any BPM workflow management solution. FlowWright is available as a cloud solution, or in an on-premise or.NET hosted environment, including AWS and Azure. It was developed in.NET Foundation C# code. All tools are browser-based and do not require plug-ins.

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.

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. You can process huge amounts of data quickly and enjoy all the benefits of the large open-source project community with the global scale Azure. You can easily migrate your big data workloads to the cloud. Open-source projects, clusters and other software are easy to set up and manage quickly. Big data clusters can reduce costs by using autoscaling and pricing levels that allow you only to use what you use. Data protection is assured by enterprise-grade security and industry-leading compliance, with over 30 certifications. Optimized components for open source technologies like Hadoop and Spark keep your up-to-date.

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.

Uber developed Horovod to make distributed deep-learning fast and easy to implement, reducing model training time from days and even weeks to minutes and hours. Horovod allows you to scale up an existing script so that it runs on hundreds of GPUs with just a few lines Python code. Horovod is available on-premises or as a cloud platform, including AWS Azure and Databricks. Horovod is also able to run on Apache Spark, allowing data processing and model-training to be combined into a single pipeline. Horovod can be configured to use the same infrastructure to train models using any framework. This makes it easy to switch from TensorFlow to PyTorch to MXNet and future frameworks, as machine learning tech stacks evolve.

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.

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.

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.

Saturn Cloud is a data science and machine learning platform flexible enough for any team supporting Python, R, and more. Scale, collaborate, and utilize built-in management capabilities to aid you when you run your code.

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.

Data analysts, engineers, scientists, and other scientists can be brought together. Automate self-service analytics and machine learning operations. Get results today, build for tomorrow. Dataiku DSS is a collaborative data science platform that allows data scientists, engineers, and data analysts to create, prototype, build, then deliver their data products more efficiently. Use notebooks (Python, R, Spark, Scala, Hive, etc.) You can also use a drag-and-drop visual interface or Python, R, Spark, Scala, Hive notebooks at every step of the predictive dataflow prototyping procedure - from wrangling to analysis and modeling. Visually profile the data at each stage of the analysis. Interactively explore your data and chart it using 25+ built in charts. Use 80+ built-in functions to prepare, enrich, blend, clean, and clean your data. Make use of Machine Learning technologies such as Scikit-Learn (MLlib), TensorFlow and Keras. In a visual UI. You can build and optimize models in Python or R, and integrate any external library of ML through code APIs.

You can build, run, and manage AI models and optimize decisions across any cloud. IBM Watson Studio allows you to deploy AI anywhere with IBM Cloud Pak®, the IBM data and AI platform. Open, flexible, multicloud architecture allows you to unite teams, simplify the AI lifecycle management, and accelerate time-to-value. ModelOps pipelines automate the AI lifecycle. AutoAI accelerates data science development. AutoAI allows you to create and programmatically build models. One-click integration allows you to deploy and run models. Promoting AI governance through fair and explicable AI. Optimizing decisions can improve business results. Open source frameworks such as PyTorch and TensorFlow can be used, as well as scikit-learn. You can combine the development tools, including popular IDEs and Jupyter notebooks. JupterLab and CLIs. This includes languages like Python, R, and Scala. IBM Watson Studio automates the management of the AI lifecycle to help you build and scale AI with trust.

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.

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.

Spark Streaming uses Apache Spark's language-integrated API for stream processing. It allows you to write streaming jobs in the same way as you write batch jobs. It supports Java, Scala, and Python. Spark Streaming recovers lost work as well as operator state (e.g. Without any additional code, Spark Streaming recovers both lost work and operator state (e.g. sliding windows) right out of the box. Spark Streaming allows you to reuse the same code for batch processing and join streams against historical data. You can also run ad-hoc queries about stream state by running on Spark. Spark Streaming allows you to create interactive applications that go beyond analytics. Apache Spark includes Spark Streaming. It is updated with every Spark release. Spark Streaming can be run on Spark's standalone mode or other supported cluster resource mangers. It also has a local run mode that can be used for development. Spark Streaming uses ZooKeeper for high availability in production.

You can quickly provision a VM with everything you need for your deep learning project on Google Cloud. Deep Learning VM Image makes it simple and quick to create a VM image containing all the most popular AI frameworks for a Google Compute Engine instance. Compute Engine instances can be launched pre-installed in TensorFlow and PyTorch. Cloud GPU and Cloud TPU support can be easily added. Deep Learning VM Image supports all the most popular and current machine learning frameworks like TensorFlow, PyTorch, and more. Deep Learning VM Images can be used to accelerate model training and deployment. They are optimized with the most recent NVIDIA®, CUDA-X AI drivers and libraries, and the Intel®, Math Kernel Library. All the necessary frameworks, libraries and drivers are pre-installed, tested and approved for compatibility. Deep Learning VM Image provides seamless notebook experience with integrated JupyterLab support.

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.

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 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.

IBM Watson Machine Learning, a full-service IBM Cloud offering, makes it easy for data scientists and developers to work together to integrate predictive capabilities into their applications. The Machine Learning service provides a set REST APIs that can be called from any programming language. This allows you to create applications that make better decisions, solve difficult problems, and improve user outcomes. Machine learning models management (continuous-learning system) and deployment (online batch, streaming, or online) are available. You can choose from any of the widely supported machine-learning frameworks: TensorFlow and Keras, Caffe or PyTorch. Spark MLlib, scikit Learn, xgboost, SPSS, Spark MLlib, Keras, Caffe and Keras. To manage your artifacts, you can use the Python client and command-line interface. The Watson Machine Learning REST API allows you to extend your application with artificial intelligence.

DL4J makes use of the most recent distributed computing frameworks, including Apache Spark and Hadoop, to accelerate training. It performs almost as well as Caffe on multi-GPUs. The libraries are open-source Apache 2.0 and maintained by Konduit and the developer community. Deeplearning4j is written entirely in Java and compatible with any JVM language like Scala, Clojure or Kotlin. The underlying computations are written using C, C++, or Cuda. Keras will be the Python API. Eclipse Deeplearning4j, a commercial-grade, open source, distributed deep-learning library, is available for Java and Scala. DL4J integrates with Apache Spark and Hadoop to bring AI to business environments. It can be used on distributed GPUs or CPUs. When training a deep-learning network, there are many parameters you need to adjust. We have tried to explain them so that Deeplearning4j can be used as a DIY tool by Java, Scala and Clojure programmers.

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.

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.

*GraphDB allows the creation of large knowledge graphs by linking diverse data and indexing it for semantic search. * GraphDB is a robust and efficient graph database that supports RDF and SPARQL. The GraphDB database supports a highly accessible replication cluster. This has been demonstrated in a variety of enterprise use cases that required resilience for data loading and query answering. Visit the GraphDB product page for a quick overview and a link to download the latest releases. GraphDB uses RDF4J to store and query data. It also supports a wide range of query languages (e.g. SPARQL and SeRQL), and RDF syntaxes such as RDF/XML and Turtle.

Find data insights that will help you solve business and research problems. IBM®, SPSS®, Statistics is a powerful statistical platform. It features a user-friendly interface, a robust set of capabilities that allow your organization to quickly extract actionable insights out of your data. Advanced statistical techniques ensure high quality and accuracy in decision making. All aspects of the analytics lifecycle, from data preparation and management to analysis, reporting and reporting, are covered. An intuitive user interface makes it easy to prepare and analyze data without writing code. You can enhance SPSS syntax with R or Python by using a variety of extensions or building your own. An integrated interface allows you to run advanced and descriptive statistics, regression analysis and decision trees. You can choose from traditional or subscription licenses with multiple capabilities depending on your needs.

Deepnote is building the best data science notebook for teams. Connect your data, explore and analyze it within the notebook with real-time collaboration and versioning. Share links to your projects with other analysts and data scientists on your team, or present your polished, published notebooks to end users and stakeholders. All of this is done through a powerful, browser-based UI that runs in the cloud.

We work closely with companies to identify the issues that prevent them from realising their potential. We create AI platforms that allow corporations to take full control and empowerment of their data. Adopting AI platforms at a reasonable cost will help you to protect your data and ensure that your privacy is protected. Enhance efficiency in businesses and increase the quality of the work done by humans. AI is used to automate repetitive or dangerous tasks. It also bypasses human intervention. This allows for faster tasks that are creative and compassionate. Machine Learning allows applications to easily provide predictive capabilities. It can create regression and classification models. It can also visualize and do clustering. It supports multiple ML libraries, including Scikit-Learn and Tensorflow. It contains around 22 algorithms for building classifications, regressions and clustering models.

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.

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.

MotherDuck is a software company founded in 2009 by a group of data geeks. We have been leaders in some of the most successful companies in data. Scale-out can be expensive and slow. Let's scale up. Big Data is dead. Long live easy data. Your laptop is faster that your data warehouse. Why wait for cloud computing? DuckDB is a slap, so let's turbocharge it. We knew that DuckDB was the next big game changer when we founded MotherDuck. Its ease-of-use, portability and lightning-fast performance combined with the community-driven innovation pace made it possible to create MotherDuck. MotherDuck is committed to helping the community, DuckDB Foundation, DuckDB Labs, and DuckDB Labs increase awareness and adoption of DuckDB. We want to help users, no matter where they are located, to use DuckDB to execute their SQL. We are a team of world-class engineers and leaders who have experience working with cloud services at AWS Databricks Elastic, Firebolt Google BigQuery, Neo4j and SingleStore.

Apache Mahout is an incredibly powerful, scalable and versatile machine-learning library that was designed for distributed data processing. It provides a set of algorithms that can be used for a variety of tasks, such as classification, clustering and recommendation. Mahout is built on top of Apache Hadoop and uses MapReduce and Spark for data processing. Apache Mahout(TM), a distributed linear-algebra framework, is a mathematically expressive Scala DSL that allows mathematicians to quickly implement their algorithms. Apache Spark is recommended as the default distributed back-end, but can be extended to work with other distributed backends. Matrix computations play a key role in many scientific and engineering applications such as machine learning, data analysis, and computer vision. Apache Mahout is designed for large-scale data processing, leveraging Hadoop and Spark.

Tabular is a table store that allows you to create an open table. It was created by the Apache Iceberg creators. Connect multiple computing frameworks and engines. Reduce query time and costs up to 50%. Centralize enforcement of RBAC policies. Connect any query engine, framework, or tool, including Athena BigQuery, Snowflake Databricks Trino Spark Python, Snowflake Redshift, Snowflake Databricks and Redshift. Smart compaction, data clustering and other automated services reduce storage costs by up to 50% and query times. Unify data access in the database or table. RBAC controls are easy to manage, enforce consistently, and audit. Centralize your security at the table. Tabular is easy-to-use and has RBAC, high-powered performance, and high ingestion under the hood. Tabular allows you to choose from multiple "best-of-breed" compute engines, based on their strengths. Assign privileges to the data warehouse database or table level.

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.

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.

Astronomer is the driving force behind Apache Airflow, the de facto standard for expressing data flows as code. Airflow is downloaded more than 4 million times each month and is used by hundreds of thousands of teams around the world. For data teams looking to increase the availability of trusted data, Astronomer provides Astro, the modern data orchestration platform, powered by Airflow. Astro enables data engineers, data scientists, and data analysts to build, run, and observe pipelines-as-code. Founded in 2018, Astronomer is a global remote-first company with hubs in Cincinnati, New York, San Francisco, and San Jose. Customers in more than 35 countries trust Astronomer as their partner for data orchestration.

Deep learning, 3D modelling, simulations and distributed analytics take days or even weeks. GPUonCLOUD’s dedicated GPU servers can do it in a matter hours. You may choose pre-configured or pre-built instances that feature GPUs with deep learning frameworks such as TensorFlow and PyTorch. MXNet and TensorRT are also available. OpenCV is a real-time computer-vision library that accelerates AI/ML model building. Some of the GPUs we have are the best for graphics workstations or multi-player accelerated games. Instant jumpstart frameworks improve the speed and agility in the AI/ML environment through effective and efficient management of the environment lifecycle.

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.

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.

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.

AWS Deep Learning AMIs are a secure and curated set of frameworks, dependencies and tools that ML practitioners and researchers can use to accelerate deep learning in cloud. Amazon Machine Images (AMIs), designed for Amazon Linux and Ubuntu, come preconfigured to include TensorFlow and PyTorch. To develop advanced ML models at scale, you can validate models with millions supported virtual tests. You can speed up the installation and configuration process of AWS instances and accelerate experimentation and evaluation by using up-to-date frameworks, libraries, and Hugging Face Transformers. Advanced analytics, ML and deep learning capabilities are used to identify trends and make forecasts from disparate health data.

Unravel makes data available anywhere: Azure, AWS and GCP, or in your own datacenter. Optimizing performance, troubleshooting, and cost control are all possible with Unravel. Unravel allows you to monitor, manage and improve your data pipelines on-premises and in the cloud. This will help you drive better performance in the applications that support your business. Get a single view of all your data stack. Unravel gathers performance data from every platform and system. Then, Unravel uses agentless technologies to model your data pipelines end-to-end. Analyze, correlate, and explore all of your cloud and modern data. Unravel's data models reveal dependencies, issues and opportunities. They also reveal how apps and resources have been used, and what's working. You don't need to monitor performance. Instead, you can quickly troubleshoot issues and resolve them. AI-powered recommendations can be used to automate performance improvements, lower cost, and prepare.

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.

A distributed data integration framework which simplifies common Big Data integration tasks such as data ingestion and replication, organization, and lifecycle management. It can be used for both streaming and batch data ecosystems. It can be run as a standalone program on a single computer. Also supports embedded mode. It can be used as a mapreduce application on multiple Hadoop versions. Azkaban is also available for the launch of mapreduce jobs. It can run as a standalone cluster, with primary and worker nodes. This mode supports high availability, and can also run on bare metals. This mode can be used as an elastic cluster in the public cloud. This mode supports high availability. Gobblin, as it exists today, is a framework that can build various data integration applications such as replication, ingest, and so on. Each of these applications are typically set up as a job and executed by Azkaban, a scheduler.

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.

Polars, which is aware of the data-wrangling habits of its users, exposes a complete Python interface, including all of the features necessary to manipulate DataFrames. This includes an expression language, which will allow you to write readable, performant code. Polars was written in Rust to provide the Rust ecosystem with a feature-complete DataFrame interface. Use it as either a DataFrame Library or as a query backend for your Data Models.

Deequ is an Apache Spark library that allows you to define "unit tests for data", which are used to measure data quality in large data sets. We welcome feedback and contributions. Deequ depends on Java 8. Deequ version 2.x is only compatible with Spark 3.1 and vice versa. If you depend on an older Spark version, please use a Deequ version 1.x (legacy version is maintained under legacy-spark-3.0). We offer legacy releases compatible to Apache Spark versions 2.2.x through 3.0.x. The Spark 2.2.x release and 2.3.x release depend on Scala 2.01 and the Spark 2.4.x.x, 3.0.x and 3.1.x depend on Scala 2.02. Deequ's purpose in unit-testing data is to identify errors before they are fed to machine learning algorithms or consuming systems. We will show you how to use our library in the simplest way possible by showing you a toy example.

Vaex.io aims to democratize the use of big data by making it available to everyone, on any device, at any scale. Your prototype is the solution to reducing development time by 80%. Create automatic pipelines for every model. Empower your data scientists. Turn any laptop into an enormous data processing powerhouse. No clusters or engineers required. We offer reliable and fast data-driven solutions. Our state-of-the art technology allows us to build and deploy machine-learning models faster than anyone else on the market. Transform your data scientists into big data engineers. We offer comprehensive training for your employees to enable you to fully utilize our technology. Memory mapping, a sophisticated Expression System, and fast Out-of-Core algorithms are combined. Visualize and explore large datasets and build machine-learning models on a single computer.