Hadoop Alternatives

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, 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. • Gain critical real-time insights to run your business • Seamlessly integrate data streaming and batch data • High performance in throughput and low-latency at petabyte scale • Fully-managed cloud service • Tiered storage to optimize cloud performance & spend • Fully-secure & enterprise-ready

More customers pick Amazon Redshift than any other cloud data warehouse. Redshift powers analytical workloads for Fortune 500 companies, startups, and everything in between. Companies like Lyft have grown with Redshift from startups to multi-billion dollar enterprises. No other data warehouse makes it as easy to gain new insights from all your data. With Redshift you can query petabytes of structured and semi-structured data across your data warehouse, operational database, and your data lake using standard SQL. Redshift lets you easily save the results of your queries back to your S3 data lake using open formats like Apache Parquet to further analyze from other analytics services like Amazon EMR, Amazon Athena, and Amazon SageMaker. Redshift is the world’s fastest cloud data warehouse and gets faster every year. For performance intensive workloads you can use the new RA3 instances to get up to 3x the performance of any cloud data warehouse.

Manage and secure the data lifecycle from the Edge to AI in any cloud or data center. Operates across all major public clouds and the private cloud with a public cloud experience everywhere. Integrates data management and analytic experiences across the data lifecycle for data anywhere. Delivers security, compliance, migration, and metadata management across all environments. Open source, open integrations, extensible, & open to multiple data stores and compute architectures. Deliver easier, faster, and safer self-service analytics experiences. Provide self-service access to integrated, multi-function analytics on centrally managed and secured business data while deploying a consistent experience anywhere—on premises or in hybrid and multi-cloud. Enjoy consistent data security, governance, lineage, and control, while deploying the powerful, easy-to-use cloud analytics experiences business users require and eliminating their need for shadow IT solutions.

The easiest way to do batch and streaming data processing. Write once, run anywhere data processing for mission-critical production workloads. Beam reads your data from a diverse set of supported sources, no matter if it’s on-prem or in the cloud. Beam executes your business logic for both batch and streaming use cases. Beam writes the results of your data processing logic to the most popular data sinks in the industry. A simplified, single programming model for both batch and streaming use cases for every member of your data and application teams. Apache Beam is extensible, with projects such as TensorFlow Extended and Apache Hop built on top of Apache Beam. Execute pipelines on multiple execution environments (runners), providing flexibility and avoiding lock-in. Open, community-based development and support to help evolve your application and meet the needs of your specific use cases.

The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance. Linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data. Cassandra's support for replicating across multiple datacenters is best-in-class, providing lower latency for your users and the peace of mind of knowing that you can survive regional outages.

Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources.

Amazon EMR is the industry-leading cloud big data platform for processing vast amounts of data using open-source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto. With EMR you can run Petabyte-scale analysis at less than half of the cost of traditional on-premises solutions and over 3x faster than standard Apache Spark. For short-running jobs, you can spin up and spin down clusters and pay per second for the instances used. For long-running workloads, you can create highly available clusters that automatically scale to meet demand. If you have existing on-premises deployments of open-source tools such as Apache Spark and Apache Hive, you can also run EMR clusters on AWS Outposts. Analyze data using open-source ML frameworks such as Apache Spark MLlib, TensorFlow, and Apache MXNet. Connect to Amazon SageMaker Studio for large-scale model training, analysis, and reporting.

Our objective it to design user friendly ETL software, which easy to deploy, can be used without training and works immediately after the installation. Our ETL software can be used by non-technical staff and there is no need to ask IT department for assistance. With our ETL software any business small and large can automate routine process and concentrate on what is important: growing the business. Design data transformations and business rules, add them to the packages together with the report, file, FTP, email and many more actions and schedule them for execution on regular basis by combining simple Package actions together Advanced ETL Processor Enterprise helps businesses and Fortune 100 companies build complex data-warehouses and automate complicated business processes with ease. Advanced ETL Processor was designed by people with years of experience of implementing data warehouses. Perform advanced data validation and transformation.

IBM Storage Scale is software-defined file and object storage that enables organizations to build a global data platform for artificial intelligence (AI), high-performance computing (HPC), advanced analytics, and other demanding workloads. Unlike traditional applications that work with structured data, today’s performance-intensive AI and analytics workloads operate on unstructured data, such as documents, audio, images, videos, and other objects. IBM Storage Scale software provides global data abstraction services that seamlessly connect multiple data sources across multiple locations, including non-IBM storage environments. It’s based on a massively parallel file system and can be deployed on multiple hardware platforms including x86, IBM Power, IBM zSystem mainframes, ARM-based POSIX client, virtual machines, and Kubernetes.

The enterprise-grade platform built on Apache Ignite that provides in-memory speed and massive scalability for data-intensive applications and real-time data access across datastores and applications. Upgrade from Ignite to GridGain with no code changes and deploy your clusters securely at global scale with zero downtime. Perform rolling upgrades of your production clusters with no impact on application availability. Replicate across globally distributed data centers to load balance workloads and prevent downtime from regional outages. Secure your data at rest and in motion, and ensure compliance with security and privacy standards. Easily integrate with your organization's authentication and authorization system. Enable full data and user activity auditing. Create automated schedules for full and incremental backups. Restore your cluster to the last stable state with snapshots and point-in-time recovery.

Google Cloud Bigtable is a fully managed, scalable NoSQL database service for large analytical and operational workloads. Fast and performant: Use Cloud Bigtable as the storage engine that grows with you from your first gigabyte to petabyte-scale for low-latency applications as well as high-throughput data processing and analytics. Seamless scaling and replication: Start with a single node per cluster, and seamlessly scale to hundreds of nodes dynamically supporting peak demand. Replication also adds high availability and workload isolation for live serving apps. Simple and integrated: Fully managed service that integrates easily with big data tools like Hadoop, Dataflow, and Dataproc. Plus, support for the open source HBase API standard makes it easy for development teams to get started.

MinIO's high-performance object storage suite is software defined and enables customers to build cloud-native data infrastructure for machine learning, analytics and application data workloads. MinIO object storage is fundamentally different. Designed for performance and the S3 API, it is 100% open-source. MinIO is ideal for large, private cloud environments with stringent security requirements and delivers mission-critical availability across a diverse range of workloads. MinIO is the world's fastest object storage server. With READ/WRITE speeds of 183 GB/s and 171 GB/s on standard hardware, object storage can operate as the primary storage tier for a diverse set of workloads ranging from Spark, Presto, TensorFlow, H2O.ai as well as a replacement for Hadoop HDFS. MinIO leverages the hard won knowledge of the web scalers to bring a simple scaling model to object storage. At MinIO, scaling starts with a single cluster which can be federated with other MinIO clusters.

PySpark is an interface for Apache Spark in Python. It not only allows you to write Spark applications using Python APIs, but also provides the PySpark shell for interactively analyzing your data in a distributed environment. PySpark supports most of Spark’s features such as Spark SQL, DataFrame, Streaming, MLlib (Machine Learning) and Spark Core. Spark SQL is a Spark module for structured data processing. It provides a programming abstraction called DataFrame and can also act as distributed SQL query engine. Running on top of Spark, the streaming feature in Apache Spark enables powerful interactive and analytical applications across both streaming and historical data, while inheriting Spark’s ease of use and fault tolerance characteristics.

SAP HANA in-memory database is for transactional and analytical workloads with any data type — on a single data copy. It breaks down the transactional and analytical silos in organizations, for quick decision-making, on premise and in the cloud. Innovate without boundaries on a database management system, where you can develop intelligent and live solutions for quick decision-making on a single data copy. And with advanced analytics, you can support next-generation transactional processing. Build data solutions with cloud-native scalability, speed, and performance. With the SAP HANA Cloud database, you can gain trusted, business-ready information from a single solution, while enabling security, privacy, and anonymization with proven enterprise reliability. An intelligent enterprise runs on insight from data – and more than ever, this insight must be delivered in real time.

Scality provides file and object storage for enterprise data management deployments of all sizes. We adapt to your environment. Traditional on-prem storage? No problem. Storage for modern cloud-native applications? We’ve got you covered. Whether it’s critical healthcare or financial data, government intelligence, digitized national treasures, streaming video content or any other valued asset — Scality has a proven track record of ensuring eleven 9s data durability and long-term protection.

As data volumes grow faster than ever, businesses struggle to get answers. Teradata Vantage™ solves this problem. Vantage uses 100 percent of available data to uncover real-time business intelligence at scale, powering the new era of Pervasive Data Intelligence. See all data from across the entire organization in one place, whenever it's needed, with preferred languages and tools. Start small and elastically scale compute or storage in areas that impact modern architecture. Vantage unifies analytics, Data Lakes, and Data Warehouses, all in the cloud to enable business intelligence. The importance of business intelligence increases. Frustration stems from four key challenges that arise when using existing data analytics platforms: Lack of proper tools and supportive environment needed to achieve quality results. Organizations do not authorize or provide proper accessibility to the necessary tools. Data preparation is difficult.

Free your apps. Simplify your ops. To win in business today, you have to be great at software. How do you improve feature velocity on the workloads that power your business? Or effectively run and manage modernized workloads on any cloud? VMware Tanzu—coupled with VMware Pivotal Labs—enables you to transform your teams and your applications, while simplifying operations across multi-cloud infrastructure: on-premises, public cloud, and edge.

The Unified Analytics Warehouse. Highest performing analytics and machine learning at extreme scale. As the criteria for data warehousing continues to evolve, tech research analysts are seeing new leaders in the drive for game-changing big data analytics. Vertica powers data-driven enterprises so they can get the most out of their analytics initiatives with advanced time-series and geospatial analytics, in-database machine learning, data lake integration, user-defined extensions, cloud-optimized architecture, and more. Our Under the Hood webcast series lets you to dive deep into Vertica features – delivered by Vertica engineers and technical experts – to find out what makes it the fastest and most scalable advanced analytical database on the market. From ride sharing apps and smart agriculture to predictive maintenance and customer analytics, Vertica supports the world’s leading data-driven disruptors in their pursuit of industry and business transformation.

Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark™ and big data workloads. Data lakes typically have multiple data pipelines reading and writing data concurrently, and data engineers have to go through a tedious process to ensure data integrity, due to the lack of transactions. Delta Lake brings ACID transactions to your data lakes. It provides serializability, the strongest level of isolation level. Learn more at Diving into Delta Lake: Unpacking the Transaction Log. In big data, even the metadata itself can be "big data". Delta Lake treats metadata just like data, leveraging Spark's distributed processing power to handle all its metadata. As a result, Delta Lake can handle petabyte-scale tables with billions of partitions and files at ease. Delta Lake provides snapshots of data enabling developers to access and revert to earlier versions of data for audits, rollbacks or to reproduce experiments.

Dremio delivers lightning-fast queries and a self-service semantic layer directly on your data lake storage. No moving data to proprietary data warehouses, no cubes, no aggregation tables or extracts. Just flexibility and control for data architects, and self-service for data consumers. Dremio technologies like Data Reflections, Columnar Cloud Cache (C3) and Predictive Pipelining work alongside Apache Arrow to make queries on your data lake storage very, very fast. An abstraction layer enables IT to apply security and business meaning, while enabling analysts and data scientists to explore data and derive new virtual datasets. Dremio’s semantic layer is an integrated, searchable catalog that indexes all of your metadata, so business users can easily make sense of your data. Virtual datasets and spaces make up the semantic layer, and are all indexed and searchable.

Kylo is an open source enterprise-ready data lake management software platform for self-service data ingest and data preparation with integrated metadata management, governance, security and best practices inspired by Think Big's 150+ big data implementation projects. Self-service data ingest with data cleansing, validation, and automatic profiling. Wrangle data with visual sql and an interactive transform through a simple user interface. Search and explore data and metadata, view lineage, and profile statistics. Monitor health of feeds and services in the data lake. Track SLAs and troubleshoot performance. Design batch or streaming pipeline templates in Apache NiFi and register with Kylo to enable user self-service. Organizations can expend significant engineering effort moving data into Hadoop yet struggle to maintain governance and data quality. Kylo dramatically simplifies data ingest by shifting ingest to data owners through a simple guided UI.

Varada’s dynamic and adaptive big data indexing solution enables to balance performance and cost with zero data-ops. Varada’s unique big data indexing technology serves as a smart acceleration layer on your data lake, which remains the single source of truth, and runs in the customer cloud environment (VPC). Varada enables data teams to democratize data by operationalizing the entire data lake while ensuring interactive performance, without the need to move data, model or manually optimize. Our secret sauce is our ability to automatically and dynamically index relevant data, at the structure and granularity of the source. Varada enables any query to meet continuously evolving performance and concurrency requirements for users and analytics API calls, while keeping costs predictable and under control. The platform seamlessly chooses which queries to accelerate and which data to index. Varada elastically adjusts the cluster to meet demand and optimize cost and performance.

Qubole is a simple, open, and secure Data Lake Platform for machine learning, streaming, and ad-hoc analytics. Our platform provides end-to-end services that reduce the time and effort required to run Data pipelines, Streaming Analytics, and Machine Learning workloads on any cloud. No other platform offers the openness and data workload flexibility of Qubole while lowering cloud data lake costs by over 50 percent. Qubole delivers faster access to petabytes of secure, reliable and trusted datasets of structured and unstructured data for Analytics and Machine Learning. Users conduct ETL, analytics, and AI/ML workloads efficiently in end-to-end fashion across best-of-breed open source engines, multiple formats, libraries, and languages adapted to data volume, variety, SLAs and organizational policies.

Talend Data Fabric’s suite of cloud services efficiently handles all your integration and integrity challenges — on-premises or in the cloud, any source, any endpoint. Deliver trusted data at the moment you need it — for every user, every time. Ingest and integrate data, applications, files, events and APIs from any source or endpoint to any location, on-premise and in the cloud, easier and faster with an intuitive interface and no coding. Embed quality into data management and guarantee ironclad regulatory compliance with a thoroughly collaborative, pervasive and cohesive approach to data governance. Make the most informed decisions based on high quality, trustworthy data derived from batch and real-time processing and bolstered with market-leading data cleaning and enrichment tools. Get more value from your data by making it available internally and externally. Extensive self-service capabilities make building APIs easy— improve customer engagement.

Hydrolix is a streaming data lake that combines decoupled storage, indexed search, and stream processing to deliver real-time query performance at terabyte-scale for a radically lower cost. CFOs love the 4x reduction in data retention costs. Product teams love 4x more data to work with. Spin up resources when you need them and scale to zero when you don’t. Fine-tune resource consumption and performance by workload to control costs. Imagine what you can build when you don’t have to sacrifice data because of budget. Ingest, enrich, and transform log data from multiple sources including Kafka, Kinesis, and HTTP. Return just the data you need, no matter how big your data is. Reduce latency and costs, eliminate timeouts, and brute force queries. Storage is decoupled from ingest and query, allowing each to independently scale to meet performance and budget targets. Hydrolix’s high-density compression (HDX) typically reduces 1TB of stored data to 55GB.

Lentiq is a collaborative data lake as a service environment that’s built to enable small teams to do big things. Quickly run data science, machine learning and data analysis at scale in the cloud of your choice. With Lentiq, your teams can ingest data in real time and then process, clean and share it. From there, Lentiq makes it possible to build, train and share models internally. Simply put, data teams can collaborate with Lentiq and innovate with no restrictions. Data lakes are storage and processing environments, which provide ML, ETL, schema-on-read querying capabilities and so much more. Are you working on some data science magic? You definitely need a data lake. In the Post-Hadoop era, the big, centralized data lake is a thing of the past. With Lentiq, we use data pools, which are multi-cloud, interconnected mini-data lakes. They work together to give you a stable, secure and fast data science environment.

Upsolver makes it incredibly simple to build a governed data lake and to manage, integrate and prepare streaming data for analysis. Define pipelines using only SQL on auto-generated schema-on-read. Easy visual IDE to accelerate building pipelines. Add Upserts and Deletes to data lake tables. Blend streaming and large-scale batch data. Automated schema evolution and reprocessing from previous state. Automatic orchestration of pipelines (no DAGs). Fully-managed execution at scale. Strong consistency guarantee over object storage. Near-zero maintenance overhead for analytics-ready data. Built-in hygiene for data lake tables including columnar formats, partitioning, compaction and vacuuming. 100,000 events per second (billions daily) at low cost. Continuous lock-free compaction to avoid “small files” problem. Parquet-based tables for fast queries.

Eliminate data silos with a single storage platform. Optimize costs with tiered storage and policy management. Authenticate data using Azure Active Directory (Azure AD) and role-based access control (RBAC). And help protect data with security features like encryption at rest and advanced threat protection. Highly secure with flexible mechanisms for protection across data access, encryption, and network-level control. Single storage platform for ingestion, processing, and visualization that supports the most common analytics frameworks. Cost optimization via independent scaling of storage and compute, lifecycle policy management, and object-level tiering. Meet any capacity requirements and manage data with ease, with the Azure global infrastructure. Run large-scale analytics queries at consistently high performance.

Dataleyk is the secure, fully-managed cloud data platform for SMBs. Our mission is to make Big Data analytics easy and accessible to all. Dataleyk is the missing link in reaching your data-driven goals. Our platform makes it quick and easy to have a stable, flexible and reliable cloud data lake with near-zero technical knowledge. Bring all of your company data from every single source, explore with SQL and visualize with your favorite BI tool or our advanced built-in graphs. Modernize your data warehousing with Dataleyk. Our state-of-the-art cloud data platform is ready to handle your scalable structured and unstructured data. Data is an asset, Dataleyk is a secure, cloud data platform that encrypts all of your data and offers on-demand data warehousing. Zero maintenance, as an objective, may not be easy to achieve. But as an initiative, it can be a driver for significant delivery improvements and transformational results.

Log analytics should not break the bank. Because most logging solutions use one or both of these technologies - Elasticsearch database and/ or Lucene index - the cost of operation is unreasonably high. ChaosSearch takes a revolutionary approach. We reinvented indexing, which allows us to pass along substantial cost savings to our customers. See for yourself with this price comparison calculator. ChaosSearch is a fully managed SaaS platform that allows you to focus on search and analytics in AWS S3 rather than spend time managing and tuning databases. Leverage your existing AWS S3 infrastructure and let us do the rest. Watch this short video to learn how our unique approach and architecture allow ChaosSearch to address the challenges of today’s data & analytic requirements. ChaosSearch indexes your data as-is, for log, SQL and ML analytics, without transformation, while auto-detecting native schemas. ChaosSearch is an ideal replacement for the commonly deployed Elasticsearch solutions.

BryteFlow builds the most efficient automated environments for analytics ever. It converts Amazon S3 into an awesome analytics platform by leveraging the AWS ecosystem intelligently to deliver data at lightning speeds. It complements AWS Lake Formation and automates the Modern Data Architecture providing performance and productivity. You can completely automate data ingestion with BryteFlow Ingest’s simple point-and-click interface while BryteFlow XL Ingest is great for the initial full ingest for very large datasets. No coding is needed! With BryteFlow Blend you can merge data from varied sources like Oracle, SQL Server, Salesforce and SAP etc. and transform it to make it ready for Analytics and Machine Learning. BryteFlow TruData reconciles the data at the destination with the source continually or at a frequency you select. If data is missing or incomplete you get an alert so you can fix the issue easily.

EMR enables you to scale the managed Hadoop clusters manually or automatically according to your business curves or monitoring metrics. EMR's storage-computation separation even allows you to terminate a cluster to maximize resource efficiency. EMR supports hot failover for CBS-based nodes. It features a primary/secondary disaster recovery mechanism where the secondary node starts within seconds when the primary node fails, ensuring the high availability of big data services. The metadata of its components such as Hive supports remote disaster recovery. Computation-storage separation ensures high data persistence for COS data storage. EMR is equipped with a comprehensive monitoring system that helps you quickly identify and locate cluster exceptions to ensure stable cluster operations. VPCs provide a convenient network isolation method that facilitates your network policy planning for managed Hadoop clusters.

Apache Storm is a free and open source distributed realtime computation system. Apache Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Apache Storm is simple, can be used with any programming language, and is a lot of fun to use! Apache Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Apache Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate. Apache Storm integrates with the queueing and database technologies you already use. An Apache Storm topology consumes streams of data and processes those streams in arbitrarily complex ways, repartitioning the streams between each stage of the computation however needed. Read more in the tutorial.

Archon Data Store™ is a powerful and secure open-source based archive lakehouse platform designed to store, manage, and provide insights from massive volumes of data. With its compliance features and minimal footprint, it enables large-scale search, processing, and analysis of structured, unstructured, & semi-structured data across your organization. Archon Data Store combines the best features of data warehouses and data lakes into a single, simplified platform. This unified approach eliminates data silos, streamlining data engineering, analytics, data science, and machine learning workflows. Through metadata centralization, optimized data storage, and distributed computing, Archon Data Store maintains data integrity. Its common approach to data management, security, and governance helps you operate more efficiently and innovate faster. Archon Data Store provides a single platform for archiving and analyzing all your organization's data while delivering operational efficiencies.

EMR is an all-in-one enterprise-ready big data platform that provides cluster, job, and data management services based on open-source ecosystems, such as Hadoop, Spark, Kafka, Flink, and Storm. Alibaba Cloud Elastic MapReduce (EMR) is a big data processing solution that runs on the Alibaba Cloud platform. EMR is built on Alibaba Cloud ECS instances and is based on open-source Apache Hadoop and Apache Spark. EMR allows you to use the Hadoop and Spark ecosystem components, such as Apache Hive, Apache Kafka, Flink, Druid, and TensorFlow, to analyze and process data. You can use EMR to process data stored on different Alibaba Cloud data storage service, such as Object Storage Service (OSS), Log Service (SLS), and Relational Database Service (RDS). You can quickly create clusters without the need to configure hardware and software. All maintenance operations are completed on its Web interface.

A distributed data integration framework that simplifies common aspects of Big Data integration such as data ingestion, replication, organization, and lifecycle management for both streaming and batch data ecosystems. Runs as a standalone application on a single box. Also supports embedded mode. Runs as an mapreduce application on multiple Hadoop versions. Also supports Azkaban for launching mapreduce jobs. Runs as a standalone cluster with primary and worker nodes. This mode supports high availability and can run on bare metals as well. Runs as an elastic cluster on public cloud. This mode supports high availability. Gobblin as it exists today is a framework that can be used to build different data integration applications like ingest, replication, etc. Each of these applications is typically configured as a separate job and executed through a scheduler like Azkaban.

Apache Druid is an open source distributed data store. Druid’s core design combines ideas from data warehouses, timeseries databases, and search systems to create a high performance real-time analytics database for a broad range of use cases. Druid merges key characteristics of each of the 3 systems into its ingestion layer, storage format, querying layer, and core architecture. Druid stores and compresses each column individually, and only needs to read the ones needed for a particular query, which supports fast scans, rankings, and groupBys. Druid creates inverted indexes for string values for fast search and filter. Out-of-the-box connectors for Apache Kafka, HDFS, AWS S3, stream processors, and more. Druid intelligently partitions data based on time and time-based queries are significantly faster than traditional databases. Scale up or down by just adding or removing servers, and Druid automatically rebalances. Fault-tolerant architecture routes around server failures.

Arcadia Data provides the first visual analytics and BI platform native to Hadoop and cloud (big data) that delivers the scale, performance, and agility business users need for both real-time and historical insights. Its flagship product, Arcadia Enterprise, was built from inception for big data platforms such as Apache Hadoop, Apache Spark, Apache Kafka, and Apache Solr, in the cloud and/or on-premises. Using artificial intelligence (AI) and machine learning (ML), Arcadia Enterprise streamlines the self-service analytics process with search-based BI and visualization recommendations. It enables real-time, high-definition insights in use cases like data lakes, cybersecurity, connected IoT devices, and customer intelligence. Arcadia Enterprise is deployed by some of the world’s leading brands, including Procter & Gamble, Citibank, Nokia, Royal Bank of Canada, Kaiser Permanente, HPE, and Neustar.

A hybrid SQL-on-Hadoop engine delivering advanced, security-rich data query across enterprise big data sources, including Hadoop, object storage and data warehouses. IBM Db2 Big SQL is an enterprise-grade, hybrid ANSI-compliant SQL-on-Hadoop engine, delivering massively parallel processing (MPP) and advanced data query. Db2 Big SQL offers a single database connection or query for disparate sources such as Hadoop HDFS and WebHDFS, RDMS, NoSQL databases, and object stores. Benefit from low latency, high performance, data security, SQL compatibility, and federation capabilities to do ad hoc and complex queries. Db2 Big SQL is now available in 2 variations. It can be integrated with Cloudera Data Platform, or accessed as a cloud-native service on the IBM Cloud Pak® for Data platform. Access and analyze data and perform queries on batch and real-time data across sources, like Hadoop, object stores and data warehouses.

Run popular open-source frameworks—including Apache Hadoop, Spark, Hive, Kafka, and more—using Azure HDInsight, a customizable, enterprise-grade service for open-source analytics. Effortlessly process massive amounts of data and get all the benefits of the broad open-source project ecosystem with the global scale of Azure. Easily migrate your big data workloads and processing to the cloud. Open-source projects and clusters are easy to spin up quickly without the need to install hardware or manage infrastructure. Big data clusters reduce costs through autoscaling and pricing tiers that allow you to pay for only what you use. Enterprise-grade security and industry-leading compliance with more than 30 certifications helps protect your data. Optimized components for open-source technologies such as Hadoop and Spark keep you up to date.

Modern lakehouse built on top of Apache Iceberg and Apache Spark. Includes: Serverless lakehouse, Serverless Spark Jobs, SQL editor, Advanced data catalog and built-in BI (or connect 3rd party BI e.g. Tableau, Looker). iomete has an extreme value proposition with compute prices is equal to AWS on-demand pricing. No mark-ups. AWS users get our platform basically for free.

SmartHub Analytics is a dedicated telecom big-data analytics platform that enables financial and subscriber-based ROI-driven use cases. Designed to support and encourage data sharing and reuse, SmartHub Analytics optimizes business performance and delivers analytics at the speed of thought. SmartHub Analytics eliminates silos and can assess, validate and model vast amounts of data from across TEOCO’s solution portfolio, including: customers, planning, optimization, service assurance, geo-location, service quality and costs. As an added analytics layer residing on top of other existing OSS & BSS solutions, SmartHub Analytics provides a standalone analytics environment with a proven return on investment (ROI), saving operators billions. We consistently uncover significant cost savings for our customers, utilizing prediction-based machine learning algorithms. SmartHub Analytics remains at the forefront of technology, delivering accelerated data analyses.

Unlock insights from all your data and build artificial intelligence (AI) solutions with Azure Databricks, set up your Apache Spark™ environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn. Azure Databricks provides the latest versions of Apache Spark and allows you to seamlessly integrate with open source libraries. Spin up clusters and build quickly in a fully managed Apache Spark environment with the global scale and availability of Azure. Clusters are set up, configured, and fine-tuned to ensure reliability and performance without the need for monitoring. Take advantage of autoscaling and auto-termination to improve total cost of ownership (TCO).

The only fully managed cloud data lakehouse designed to ingest from all your data sources in minutes and support all your query engines at scale, for a fraction of the cost. Ingest from databases and event streams at TB-scale in near real-time, with the simplicity of fully managed pipelines. Query your data with any engine, and support all your use cases including BI, real-time analytics, and AI/ML. Cut your costs by 50% or more compared to cloud data warehouses and ETL tools with simple usage-based pricing. Deploy in minutes without engineering overhead with a fully managed, highly optimized cloud service. Unify your data in a single source of truth and eliminate the need to copy data across data warehouses and lakes. Use the right table format for the job, with omnidirectional interoperability between Apache Hudi, Apache Iceberg, and Delta Lake. Quickly configure managed pipelines for database CDC and streaming ingestion.

Simplify big data operations and build intelligent knowledge libraries with Data Lake Governance Center (DGC), a one-stop data lake operations platform that manages data design, development, integration, quality, and assets. Build an enterprise-class data lake governance platform with an easy-to-use visual interface. Streamline data lifecycle processes, utilize metrics and analytics, and ensure good governance across your enterprise. Define and monitor data standards, and get real-time alerts. Build data lakes quicker by easily setting up data integrations, models, and cleaning rules, to enable the discovery of new reliable data sources. Maximize the business value of data. With DGC, end-to-end data operations solutions can be designed for scenarios such as smart government, smart taxation, and smart campus. Gain new insights into sensitive data across your entire organization. DGC allows enterprises to define business catalogs, classifications, and terms.

Collect, transform and integrate your enterprise’s security data to enable Palo Alto Networks solutions. Radically simplify security operations by collecting, transforming and integrating your enterprise’s security data. Facilitate AI and machine learning with access to rich data at cloud native scale. Significantly improve detection accuracy with trillions of multi-source artifacts. Cortex XDR™ is the industry’s only prevention, detection, and response platform that runs on fully integrated endpoint, network and cloud data. Prisma™ Access protects your applications, remote networks and mobile users in a consistent manner, wherever they are. A cloud-delivered architecture connects all users to all applications, whether they’re at headquarters, branch offices or on the road. The combination of Cortex™ Data Lake and Panorama™ management delivers an economical, cloud-based logging solution for Palo Alto Networks Next-Generation Firewalls. Zero hardware, cloud scale, available anywhere.

Qwak simplifies the productionization of machine learning models at scale. Qwak’s [ML Engineering Platform] empowers data science and ML engineering teams to enable the continuous productionization of models at scale. By abstracting the complexities of model deployment, integration and optimization, Qwak brings agility and high-velocity to all ML initiatives designed to transform business, innovate, and create competitive advantage. Qwak build system allows data scientists to create an immutable, tested production-grade artifact by adding "traditional" build processes. Qwak build system standardizes a ML project structure that automatically versions code, data, and parameters for each model build. Different configurations can be used to build different builds. It is possible to compare builds and query build data. You can create a model version using remote elastic resources. Each build can be run with different parameters, different data sources, and different resources. Builds c

Create data products from any source in seconds, without moving the data. Make them available to anyone, while maintaining complete control. Deliver powerful experiences to unlock value. Enhance your data mesh by seamlessly sharing, discovering, and governing data across domains. Foster collaboration and accelerate innovation with unified access to high-quality data products. Provide governed access to AI models for any user. Control how data interacts with AI to safeguard intellectual property. Automate AI workflows to rapidly integrate and iterate new capabilities. Access and build data products from Snowflake without moving any data. Experience the ease of getting more from your data. Make it easy for anyone to analyze data and remove the need for centralized provisioning of infrastructure and tools. Data products are magically integrated with tools, to ensure governance and accelerate outcomes.

The Qlik Data Integration platform for managed data lakes automates the process of providing continuously updated, accurate, and trusted data sets for business analytics. Data engineers have the agility to quickly add new sources and ensure success at every step of the data lake pipeline from real-time data ingestion, to refinement, provisioning, and governance. A simple and universal solution for continually ingesting enterprise data into popular data lakes in real-time. A model-driven approach for quickly designing, building, and managing data lakes on-premises or in the cloud. Deliver a smart enterprise-scale data catalog to securely share all of your derived data sets with business users.

The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals. Databricks combines generative AI with the unification benefits of a lakehouse to power a Data Intelligence Engine that understands the unique semantics of your data. This allows the Databricks Platform to automatically optimize performance and manage infrastructure in ways unique to your business. The Data Intelligence Engine understands your organization’s language, so search and discovery of new data is as easy as asking a question like you would to a coworker.