Alternatives to Tencent Cloud Elastic MapReduce

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.

StarTree Cloud is a fully-managed real-time analytics platform designed for OLAP at massive speed and scale for user-facing applications. Powered by Apache Pinot, StarTree Cloud provides enterprise-grade reliability and advanced capabilities such as tiered storage, scalable upserts, 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.

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.

The fundamental idea of YARN is to split up the functionalities of resource management and job scheduling/monitoring into separate daemons. The idea is to have a global ResourceManager, (RM), and a per-application ApplicationMaster, (AM). An application can be a single job, or a DAG (distributed array of jobs). The data-computation framework is formed by the NodeManager and the ResourceManager. The ResourceManager is the ultimate authority who arbitrates the allocation of resources among all applications in the system. The NodeManager is the per-machine framework agent who is responsible for containers, monitoring their resource usage (cpu, memory, disk, network) and reporting the same to the ResourceManager/Scheduler. The per-application ApplicationMaster, which is in essence a framework-specific library, is responsible for negotiating resources from ResourceManager and working with NodeManagers to execute and monitor tasks.

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.

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.

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.

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.

A single storage platform can eliminate data silos. Tiered storage and policy management can help you reduce costs. Azure Active Directory (Azure AD), and role-based access control(RBAC) can authenticate data. You can also help protect your data with advanced threat protection and encryption at rest. Flexible mechanisms provide protection for data access, encryption, network-level control, and more. Highly secure. A single storage platform that supports all the most popular analytics frameworks. Cost optimization through independent scaling of storage, compute, lifecycle management and object-level Tiering. With the Azure global infrastructure, you can meet any capacity requirement and manage data with ease. Large-scale analytics queries run at high performance.

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.

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.

You can query billions upon billions of rows with an in-memory columnar database and MPP architecture. Queries are distributed across all cluster nodes, allowing for linear scaling and advanced analytics. The fastest database for data analytics is made up of MPP, columnar storage, and in-memory. You can analyze data anywhere it is stored, whether you are using SaaS, cloud, hybrid, or on-premises deployments. Automatic query tuning reduces overhead and maintenance. You get more power for a fraction of the normal infrastructure costs with seamless integrations and performance efficiency. This social networking company was able to increase its performance by using smart, in-memory query processing. They processed 10B data sets per year. A single data repository and speed-engine to accelerate critical analytics, improving patient outcomes and the bottom line.

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.

Alibaba Cloud launched DataWorks, a Big Data platform product. It offers Big Data development, data permission management and offline job scheduling. DataWorks is easy to use and does not require any special cluster setup or management. To create a workflow, drag and drop nodes. Online editing and debugging of code is possible. You can also ask other developers to join your project. Data integration, MaxCompute SQL and MaxCompute MS, machine learning, shell tasks, and MaxCompute MR are supported. To prevent service interruptions, task monitoring is supported. It sends alarms when errors are detected. It can run millions of tasks simultaneously and supports hourly, daily and weekly schedules. DataWorks is the best platform to build big data warehouses. It also offers comprehensive data warehousing and support services. DataWorks offers a complete solution for data aggregation and processing, as well as data governance and data services.

In-data computing is at the core of our innovation. It's a technology that allows us to process large quantities of data efficiently. BigObject, our flagship product, is a time-series database that was developed to handle massive data at high speed. Our core technology, in-data computing enabled us to launch BigObject. It can handle non-stop data streams and all their aspects quickly and continuously. BigObject is an in-data database designed for high-speed data storage and analysis. It has excellent performance and powerful query capabilities. It extends the relational data model to a time series model structure and uses in-data computing for database performance optimization. Our core technology is a model abstract in which all data are stored in an infinite memory space.

You can automate the cluster scaling and deployment on demand, and concentrate on processing, analysis and reporting big data. Our operations team is able to handle cluster operations thanks to the accumulation of massively distributed computing technologies over many years. It automatically scales clusters up to improve computing capability in peak periods and down to reduce costs in the valley period. It provides a management console that facilitates cluster management, template customization and task submission. By deploying with the BCC it can focus on its own business during a busy period and help the BMR to calculate the big data when there is free time. This reduces the overall IT expenditure.

NFVgrid automates provisioning, monitoring, analysis, and life-cycle management of Virtual Network Function appliances. It is a single system that manages all aspects of virtual network function appliances. The NFVgrid portal offers a seamless user experience. The dashboard displays all virtual appliances and services that customers can roll-out or terminate. NFVgrid automatically connects virtual appliances to the networks of your choice and deploys them with pre-provisioned settings. Virtual network appliances can be accessed via a web portal or CLI for advanced settings. NFVgrid is a network that can work in isolation. We created it with a rich set RESTful APIs to allow easy integration with OSS, BSS, and billing systems. NFVgrid offers performance monitoring functions and a meaningful representation for the various types of analytical data for traffic passing through the network or a specific VM.

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.

Rocket iCluster high availability/disaster recovery (HA/DR) solutions ensure uninterrupted operation for your IBM i applications, providing continuous access by monitoring, identifying, and self-correcting replication problems. iCluster’s multi-cluster administration console monitors real-time events on the classic web UI and the green screen. Rocket iCluster reduces the downtime associated with unexpected IBM i system disruptions by using fault-tolerant object-level replication in real-time. In the event of a failure, you can quickly bring a "warm mirror" of a clustered IBM i into service. iCluster disaster-recovery software ensures a highly available environment by allowing business applications to simultaneously access both the master and replicated data. This setup allows you offload critical business functions such as running queries and reports, as well as ETL and EDI tasks, from your secondary system, without affecting the performance of your primary system.

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.

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.

ClusterVisor, an HPC cluster system, provides comprehensive tools to deploy, provision, manage, monitor, and maintain high-performance computing clusters through their entire lifecycle. It offers flexible deployment options, such as appliance deployment, which decouples the cluster management from head node and enhances system resilience. The platform includes LogVisor AI - an integrated log file analyzer that uses AI to classify logs according to severity. This allows for the creation of actionable alarms. ClusterVisor provides a set of tools to manage nodes, support user and group accounts, and provide customizable dashboards that allow comparisons and visualizations across multiple nodes and devices. It offers disaster recovery capabilities, storing system images to reinstall nodes. It also provides an intuitive web-based tool for rack diagramming, and allows comprehensive statistics and monitoring.

WarpStream, an Apache Kafka compatible data streaming platform, is built directly on object storage. It has no inter-AZ network costs, no disks that need to be managed, and it's infinitely scalable within your VPC. WarpStream is deployed in your VPC as a stateless, auto-scaling binary agent. No local disks are required to be managed. Agents stream data directly into and out of object storage without buffering on local drives and no data tiering. Instantly create new "virtual" clusters in our control plan. Support multiple environments, teams or projects without having to manage any dedicated infrastructure. WarpStream is Apache Kafka protocol compatible, so you can continue to use your favorite tools and applications. No need to rewrite or use a proprietary SDK. Simply change the URL of your favorite Kafka library in order to start streaming. Never again will you have to choose between budget and reliability.

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.

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.

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.

In-Memory Computing Platform. Digital world is different. Microseconds are important. The world's most important organizations rely on us for powering their most sensitive applications at scale. If they meet the current requirement for immediate access, new data-enabled apps can transform your business. Hazelcast solutions can be used to complement any database and deliver results that are much faster than traditional systems of record. Hazelcast's distributed architecture ensures redundancy and continuous cluster up-time, as well as always available data to support the most demanding applications. The capacity grows with demand without compromising performance and availability. The cloud delivers the fastest in-memory data grid and third-generation high speed event processing.

Lentiq is a data lake that allows small teams to do big tasks. You can quickly run machine learning, data science, and data analysis at scale in any cloud. Lentiq allows your teams to ingest data instantly and then clean, process, and share it. Lentiq allows you to create, train, and share models within your organization. Lentiq allows data teams to collaborate and invent with no restrictions. Data lakes are storage and process environments that provide ML, ETL and schema-on-read querying capabilities. Are you working on data science magic? A data lake is a must. The big, centralized data lake of the Post-Hadoop era is gone. Lentiq uses data pools, which are interconnected, multi-cloud mini-data lakes. They all work together to provide a stable, secure, and fast data science environment.

Yandex Data Proc creates and configures Spark clusters, Hadoop clusters, and other components based on the size, node capacity and services you select. Zeppelin Notebooks and other web applications can be used to collaborate via a UI Proxy. You have full control over your cluster, with root permissions on each VM. Install your own libraries and applications on clusters running without having to restart. Yandex Data Proc automatically increases or decreases computing resources for compute subclusters according to CPU usage indicators. Data Proc enables you to create managed clusters of Hive, which can reduce failures and losses due to metadata not being available. Save time when building ETL pipelines, pipelines for developing and training models, and describing other iterative processes. Apache Airflow already includes the Data Proc operator.

For more four decades, IRI CoSort has defined the state-of-the-art in big data sorting and transformation technology. From advanced algorithms to automatic memory management, and from multi-core exploitation to I/O optimization, there is no more proven performer for production data processing than CoSort. CoSort was the first commercial sort package developed for open systems: CP/M in 1980, MS-DOS in 1982, Unix in 1985, and Windows in 1995. Repeatedly reported to be the fastest commercial-grade sort product for Unix. CoSort was also judged by PC Week to be the "top performing" sort on Windows. CoSort was released for CP/M in 1978, DOS in 1980, Unix in the mid-eighties, and Windows in the early nineties, and received a readership award from DM Review magazine in 2000. CoSort was first designed as a file sorting utility, and added interfaces to replace or convert sort program parameters used in IBM DataStage, Informatica, MF COBOL, JCL, NATURAL, SAS, and SyncSort. In 1992, CoSort added related manipulation functions through a control language interface based on VMS sort utility syntax, which evolved through the years to handle structured data integration and staging for flat files and RDBs, and multiple spinoff products.

Scribble Data allows organizations to enrich their data and transform it to enable reliable, fast decision-making for business problems. Data-driven decision support for your business. Data-to-decision platform that allows you to generate high-fidelity insights and automate decision-making. Machine learning and advanced analytics can solve your business decision-making problems. Enrich will do the heavy lifting so you can focus on the important tasks and Enrich will take care of the rest. You can use customized data-driven workflows to make data consumption easy and reduce dependence on machine learning and data science engineering teams. With feature engineering capabilities that can prepare large volumes of complex data at scale, you can go from concept to operational product in a matter of weeks.

What makes eXtremeDB platform independent? - Hybrid storage of data. Unlike other IMDS databases, eXtremeDB databases are all-in-memory or all-persistent. They can also have a mix between persistent tables and in-memory table. eXtremeDB's Active Replication Fabric™, which is unique to eXtremeDB, offers bidirectional replication and multi-tier replication (e.g. edge-to-gateway-to-gateway-to-cloud), compression to maximize limited bandwidth networks and more. - Row and columnar flexibility for time series data. eXtremeDB supports database designs which combine column-based and row-based layouts in order to maximize the CPU cache speed. - Client/Server and embedded. eXtremeDB provides data management that is fast and flexible wherever you need it. It can be deployed as an embedded system and/or as a clients/server database system. eXtremeDB was designed for use in resource-constrained, mission-critical embedded systems. Found in over 30,000,000 deployments, from routers to satellites and trains to stock market world-wide.

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.

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.

Spend your time developing applications, not maintaining infrastructure. Managed Service for Apache Kafka manages Zookeeper brokers, clusters and updates their versions. Distribute cluster brokers over different availability zones, and set the replication factors to ensure fault tolerance. The service will analyze the metrics and status and replace the cluster if any node fails. You can adjust the number of messages, log cleanup policy and compression type for each topic to optimize computing, network and disk resources. You can add brokers with a single click to your cluster to improve performance or change the class for high-availability servers without stopping them and losing any data.

Storidge was founded on the belief that enterprise storage should be easy to manage. We have a completely different approach to Kubernetes storage, and Docker volumes. It automates storage operations for orchestration system, such as Kubernetes or Docker Swarm. This saves time and money, eliminating the need to hire expensive expertise. This allows developers to concentrate their efforts on creating applications and creating value, while operators can focus on delivering the value quicker to market. In seconds, you can add persistent storage to your single-node test cluster. You can deploy storage infrastructure as code and reduce operator decisions while optimizing operational workflow. Automated updates, provisioning and recovery. Auto failover and automatic data restoration keep your critical apps and databases running.

ProxmoxVE is an open-source platform that enables all-inclusive enterprise virtualization. It tightly integrates KVM hypervisor, LXC containers and software-defined storage. It also offers networking functionality and easy management of high availability clusters with the built-in web interface.

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.

Varada's adaptive and dynamic big data indexing solution allows you to balance cost and performance with zero data-ops. Varada's big data indexing technology is a smart acceleration layer for your data lake. It remains the single source and truth and runs in the customer's cloud environment (VPC). Varada allows data teams to democratize data. It allows them to operationalize the entire data lake and ensures interactive performance without the need for data to be moved, modelled, or manually optimized. Our ability to dynamically and automatically index relevant data at the source structure and granularity is our secret sauce. Varada allows any query to meet constantly changing performance and concurrency requirements of users and analytics API calls. It also keeps costs predictable and under control. The platform automatically determines which queries to speed up and which data to index. Varada adjusts the cluster elastically to meet demand and optimize performance and cost.

*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.

kdb Insights, a cloud native, high-performance analytics solution designed for real-time data analysis of streaming and historical data, is a platform that can be used to analyze both streams and historical information. It allows for intelligent decision making regardless of data volume and velocity. It offers unmatched performance and price, and delivers analytics up to 100-fold faster than other solutions. The platform allows interactive data visualization via real-time dashboards to facilitate instantaneous insight and decision-making. It also integrates machine-learning models to predict and cluster structured data, detect patterns, score it, and enhance AI capabilities for time-series datasets. kdb Insights is scalable enough to handle large volumes of real-time data and historical data. This has been proven with volumes up to 110 Terabytes per Day. Its simple data intake and quick setup accelerate time-to value. Native support for q SQL and Python is also available, as well as compatibility with other programming languages via RESTful interfaces.

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.

Trino is an engine that runs at incredible speeds. Fast-distributed SQL engine for big data analytics. Helps you explore the data universe. Trino is an extremely parallel and distributed query-engine, which is built from scratch for efficient, low latency analytics. Trino is used by the largest organizations to query data lakes with exabytes of data and massive data warehouses. Supports a wide range of use cases including interactive ad-hoc analysis, large batch queries that take hours to complete, and high volume apps that execute sub-second queries. Trino is a ANSI SQL query engine that works with BI Tools such as R Tableau Power BI Superset and many others. You can natively search data in Hadoop S3, Cassandra MySQL and many other systems without having to use complex, slow and error-prone copying processes. Access data from multiple systems in a single query.

Apache Helix is a generic cluster management framework that automates the management of distributed, replicated, and partitioned resources hosted on a cluster. Helix automates the reassignment and reconfiguration of resources in case of node failure, recovery, cluster expansion, or reconfiguration. Cluster management is the first step to understanding Helix. For the following reasons, a distributed system is typically run on multiple nodes: Scalability, fault tolerance, load balancencing, and scalability. Each node is responsible for one or more of the cluster's primary functions, such as serving and storing data, producing and consuming data streams, etc. Helix is the global brain of your system once it has been configured. It is designed to make decisions that are not possible in isolation. Although it is possible to integrate these functions into a distributed system, it can complicate the code.

Use a global load balancing solution that is expertly designed and engineered to ensure fast performance. The DNS is fully configurable through APIs. It also offers DDoS protection, and there are no appliances to maintain. Direct traffic to the nearest instance of an application and/or route it for GDPR compliance. Split workloads across compute instances. Reroute clients if resource instances are degraded or fail. Disaster recovery allows you to maintain high availability. Automatically detect primary site problems, get zero-touch failureover, and dynamically fail over applications to designated or available instances. Disaster recovery and cloud-based DNS management will ease the burden of your operations and development team. F5's intelligent DNS cloud-based with global server load balancing, or GSLB, efficiently directs traffic across environments, performs health check, and automates response to activities and events in order to maintain high performance.

Easy deployment of a high-availability solution for disaster recovery, failover within and between cloud regions with zero downtime during maintenance. Multiple master databases distributed across multiple locations can improve performance and availability. Keep local data local, and control which tables will be globally replicated and which will remain local. Support higher throughput if workloads threaten to exceed the available compute capacity. pgEdge platform is available on-premises, or through self-managed cloud provider accounts. Supports a wide range of OS and hardware combinations. Enterprise-class support available. Edge Platform nodes that are self-hosted can be used as part of a Postgres cluster in pgEdge cloud.

Dremio provides lightning-fast queries as well as a self-service semantic layer directly to your data lake storage. No data moving to proprietary data warehouses, and no cubes, aggregation tables, or extracts. Data architects have flexibility and control, while data consumers have self-service. Apache Arrow and Dremio technologies such as Data Reflections, Columnar Cloud Cache(C3), and Predictive Pipelining combine to make it easy to query your data lake storage. An abstraction layer allows IT to apply security and business meaning while allowing analysts and data scientists access data to explore it and create new virtual datasets. Dremio's semantic layers is an integrated searchable catalog that indexes all your metadata so business users can make sense of your data. The semantic layer is made up of virtual datasets and spaces, which are all searchable and indexed.

The Leading Distributed SQL Database with High Performance. Open source, cloud native relational DB that powers global, internet-scale applications. Single-Digit Millisecond latency Create lightning fast cloud applications by serving queries directly to the DB. Massive Scale You can achieve millions of transactions per second and store multiple TTB's of data per Node. Geo-Distribution You can deploy across regions and clouds using synchronous or multimaster replication. Cloud Native Architectures. YugabyteDB makes it easy to develop, deploy, and operate modern applications faster than ever. Develop developer agility. Leverage full power of PostgreSQL-compatible SQL and distributed ACID transactions. Operate resilient services. Ensure continuous availability, even when the underlying storage, compute, or network fails. Scale On-Demand. You can add or remove nodes as you wish. Over-provisioned clusters are not a good idea. Lower User Latency.

Iceberg is an efficient format for large analytical tables. Iceberg brings the simplicity and reliability of SQL tables to the world of big data. It also allows engines like Spark, Trino Flink Presto Hive Impala and Impala to work safely with the same tables at the same time. Iceberg supports SQL commands that are flexible to merge new data, update rows, and perform targeted deletions. Iceberg can eagerly write data files to improve read performance or it can use delete-deltas for faster updates. Iceberg automates the tedious, error-prone process of generating partition values for each row in a table. It also skips unnecessary files and partitions. There are no extra filters needed for fast queries and the table layout is easily updated when data or queries change.

Google Cloud Bigtable provides a fully managed, scalable NoSQL data service that can handle large operational and analytical workloads. Cloud Bigtable is fast and performant. It's the storage engine that grows with your data, from your first gigabyte up to a petabyte-scale for low latency applications and high-throughput data analysis. Seamless scaling and replicating: You can start with one cluster node and scale up to hundreds of nodes to support peak demand. Replication adds high availability and workload isolation to live-serving apps. Integrated and simple: Fully managed service that easily integrates with big data tools such as Dataflow, Hadoop, and Dataproc. Development teams will find it easy to get started with the support for the open-source HBase API standard.