Best Data Management Software for Hadoop

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

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

Warp 10 is a modular open source platform that collects, stores, and allows you to analyze time series and sensor data. Shaped for the IoT with a flexible data model, Warp 10 provides a unique and powerful framework to simplify your processes from data collection to analysis and visualization, with the support of geolocated data in its core model (called Geo Time Series). Warp 10 offers both a time series database and a powerful analysis environment, which can be used together or independently. It will allow you to make: statistics, extraction of characteristics for training models, filtering and cleaning of data, detection of patterns and anomalies, synchronization or even forecasts. The Platform is GDPR compliant and secure by design using cryptographic tokens to manage authentication and authorization. The Analytics Engine can be implemented within a large number of existing tools and ecosystems such as Spark, Kafka Streams, Hadoop, Jupyter, Zeppelin and many more. From small devices to distributed clusters, Warp 10 fits your needs at any scale, and can be used in many verticals: industry, transportation, health, monitoring, finance, energy, etc.

Promethium empowers data and analytics teams to work smarter, so they can keep up with growing data volumes and business requirements. It is not enough to connect to a data lake or data warehouse to access raw data. Datasets require a lot more work from data teams! Data teams are not growing as fast as the data volumes or the business demand for data. Promethium makes overloaded data teams more efficient and can deliver more quickly. Reduce your dependence on ETL. Access data wherever it is. It is easier to move less data, which saves you time and money. Promethium can be done by one person in minutes. This is a significant improvement on the time and effort required for a team of six or more tools. Connect and catalog data sources, create cross-source datasets, and query them with just a few clicks. There is less custom code and less ETL. Validate data is accurate in real-time, and not after months of work. Instantly share work to make it reuseable, rather than recreate it.

Oracle Big Data SQL Cloud Service allows organizations to instantly analyze data across Apache Hadoop and NoSQL. This service leverages their existing SQL skills, security policy, and applications with extreme speed. Big Data SQL allows you to simplify data science and unlock data lakes. Big Data SQL provides users with a single place to store and secure data in Hadoop, NoSQL systems, and Oracle Database. Seamless metadata integration, and queries that combine data from Oracle Database and Hadoop and NoSQL database data. Automated mappings can be done from metadata stored in HCatalog or the Hive Metastore to Oracle Tables using utility and conversion routines. Administrators have the ability to set enhanced access parameters that allow them to control data access behavior and column mapping. Multiple cluster support allows one Oracle Database to query multiple Hadoop clusters or NoSQL systems.

ThinkData Works provides a robust catalog platform for discovering, managing, and sharing data from both internal and external sources. Enrichment solutions combine partner data with your existing datasets to produce uniquely valuable assets that can be shared across your entire organization. The ThinkData Works platform and enrichment solutions make data teams more efficient, improve project outcomes, replace multiple existing tech solutions, and provide you with a competitive advantage.

Our cloud account connections (AWS, Azure, and GCP) are easy to establish with our agentless data discovery platform and scanning platform. There is nothing to install or manage. All native cloud data stores are supported, whether they are structured or unstructured. Normalyze scans your cloud accounts for both structured and unstructured data. It only collects metadata to be added to the Normalyze graph. During scanning, no sensitive data is collected. A graph of trust and access relationships is displayed in real-time. It includes fine-grained context, process names, data store fingerprints, IAM role and policies. Locate all sensitive data stores, identify all access paths, and score possible breach paths based upon sensitivity, volume, or permissions. This will allow you to quickly show all breaches that are waiting to happen. Identify sensitive data-based industry profiles like PCI, HIPAA and GDPR.

The classic data lake is often reduced to simple but inexpensive raw data storage. This neglects important aspects like data quality, security, and transformation. These topics are left to data scientists who spend up to 80% of their time cleaning, understanding, and acquiring data before they can use their core competencies. Additionally, traditional Data Lakes are often implemented in different departments using different standards and tools. This makes it difficult to implement comprehensive analytical use cases. Smart Data Lakes address these issues by providing methodical and architectural guidelines as well as an efficient tool to create a strong, high-quality data foundation. Smart Data Lakes are the heart of any modern analytics platform. They integrate all the most popular Data Science tools and open-source technologies as well as AI/ML. Their storage is affordable and scalable, and can store both structured and unstructured data.

Reframe your perception of time with data analytics. Instantly access the data of your business and work directly in your dashboard, without having to go back and forth with your IT team. Indexima DataHub is a new space where operational and functional users can instantly access their data. Indexima's unique indexing engine, combined with machine learning, allows businesses to quickly and easily access their data. The robust and scalable solution allows businesses to query their data directly from the source in volumes of up to tens billions of rows within milliseconds. With our Indexima platform, users can implement instant analytics for all their data with just one click. Indexima’s new ROI and TCO Calculator will help you determine the ROI of your data platform in just 30 seconds. Infrastructure costs, project deployment times, and data engineering cost, while boosting analytical performances.

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.

Impala offers low latency, high concurrency, and a wide range of storage options, including Iceberg and open data formats. Impala scales linearly in multitenant environments. Impala integrates native Hadoop security, Kerberos authentication, and the Ranger module to ensure that the correct users and applications have access to the right data. Utilize the same file and data formats and metadata, security, and resource management frameworks as your Hadoop deployment, with no redundant infrastructure or data conversion/duplication. Impala uses the same metadata driver and ODBC driver as Apache Hive. Impala, like Hive, supports SQL. You don't need to reinvent the wheel. Impala allows more users to interact with data, whether they are using SQL queries or BI apps, through a single repository. Metadata is also stored from the source of the data until it has been analyzed.

Apache Phoenix combines the best of both worlds to enable OLTP and operational analysis in Hadoop. This allows for low-latency Hadoop applications. HBase is used as the backing store for Apache Phoenix, which combines the power of SQL and JDBC with ACID transaction support and flexibility of late bound, schema-on read capabilities from the NoSQL realm. Apache Phoenix is fully compatible with other Hadoop tools such as Spark and Hive. It also integrates with Pig, Flume and Map Reduce. Become the trusted Hadoop data platform for OLTP, operational analytics and Hadoop via well-defined APIs. Apache Phoenix compiles your SQL query into a series HBase scans and orchestrates their running to produce regular JDBC results sets. Direct use of HBase API along with coprocessors, custom filters and other tools results in performance of milliseconds or seconds for small queries.

Our intelligent data and analytics platform will help you scale faster by overcoming application silos, cost overruns, and skill obsolescence. A platform that is intelligently designed to perform advanced analytics and data management. Scales across all technology landscapes. Our architecture understands the data flow and transformations throughout its entire lifecycle. Developing future-proof enterprise AI apps. A highly extensible and modular platform that allows the handling of multiple components. Scalable architecture with multi-tenant design. Advanced data visualization makes it easy to analyze complex data structures. This results in enhanced enterprise AI apps in a low-code, intuitive platform. Our hybrid multi-cloud platform was built using community open source software, making it highly adaptable, secure, and low-cost.

Apache Trafodion, a webscale SQL on Hadoop solution, enables transactional or operational workloads on Apache Hadoop. Trafodion is built on Hadoop's elasticity, scalability and flexibility. Trafodion enhances Hadoop by providing guaranteed transactional integrity. This allows new types of big data applications run on Hadoop. Support for ANSI SQL in its entirety. JDBC/ODBC connectivity on Linux/Windows clients. Distributed ACID transactions protection across multiple tables, rows, and statements. Compile-time and runtime optimizations improve performance for OLTP workloads. Support for large data sets with a parallel-aware queries optimizer. Reuse existing SQL skills to improve developer productivity. Distributed ACID transactions ensure data consistency across multiple rows or tables. Interoperability of existing tools and applications. Hadoop and Linux distributions are neutral. Add to your existing Hadoop infrastructure.

Empower all levels of skill to make data-driven decisions, and take action when it is most important. Deeper interactivity. Broader context Lightning fast. No one else can match it. Qlik's unique Associative technology is unrivalled in its ability to power our industry-leading analytics experience. All your users can explore at their own pace with hyperfast calculations. Always in context and at scale. It's big. Qlik Sense goes beyond the limitations of query-based analytics or dashboards offered by competitors. Insight Advisor in Qlik Sense employs AI to help users understand and use data better, minimizing cognitive bias, increasing discovery, and elevating data literacy. Organizations need to have a dynamic relationship with the information that is relevant at the moment. Traditional passive BI is not enough.

Alteryx AI Platform will help you enter a new age of analytics. Empower your organization through automated data preparation, AI powered analytics, and accessible machine learning - all with embedded governance. Welcome to a future of data-driven decision making for every user, team and step. Empower your team with an intuitive, easy-to-use user experience that allows everyone to create analytical solutions that improve productivity and efficiency. Create an analytics culture using an end-toend cloud analytics platform. Data can be transformed into insights through self-service data preparation, machine learning and AI generated insights. Security standards and certifications are the best way to reduce risk and ensure that your data is protected. Open API standards allow you to connect with your data and applications.

Couchbase, unlike other NoSQL database, provides a multicloud to edge enterprise-class database that offers robust capabilities for business-critical apps on a highly available and scalable platform. Couchbase is a distributed cloud native database that runs on any cloud. It can be managed by the customer or fully managed. Couchbase is built using open standards and combines the best of NoSQL and SQL with the power and familiarity that mainframes and relational databases provide. Couchbase Server is an open-source, multipurpose distributed database. It combines the best of relational databases, such as SQL, ACID transactions, and JSON, with a foundation which is fast and scalable. It is used in many industries for things such as user profiles, dynamic catalogs, GenAI applications, vector search, caching at high speed, and more.

The Unified Analytics Warehouse. The Unified Analytics Warehouse is the best place to find high-performing analytics and machine learning at large scale. Tech research analysts are seeing new leaders as they strive to deliver game-changing big data analytics. Vertica empowers data-driven companies so they can make the most of their analytics initiatives. It offers advanced time-series, geospatial, and machine learning capabilities, as well as data lake integration, user-definable extensions, cloud-optimized architecture and more. Vertica's Under the Hood webcast series allows you to dive into the features of Vertica - delivered by Vertica engineers, technical experts, and others - and discover what makes it the most scalable and scalable advanced analytical data database on the market. Vertica supports the most data-driven disruptors around the globe in their pursuit for industry and business transformation.

ICONICS' Hyper historian™, a 64-bit high-speed, reliable and robust historian, is advanced 64-bit. Hyper Historian's high compression algorithm provides exceptional performance and efficient use of resources. It is designed for mission-critical applications. Hyper Historian can be integrated with our ISA 95-compliant asset databank and the most recent big data technologies, such as Azure SQL, Microsoft Data Lakes and Kafka. Hyper Historian is the most secure and efficient real-time plant historian available for any Microsoft operating system. Hyper Historian has a module that allows users to manually or automatically insert data. This allows users to import log data from other historians, databases, and intermittently connected field devices or equipment. This greatly increases the reliability of data capture, even in the face of network disruptions. Leverage rapid collection for enterprise-wide storage.

Mage Sensitive Data Discovery module can help you uncover hidden data locations in your company. You can find data hidden in any type of data store, whether it is structured, unstructured or Big Data. Natural Language Processing and Artificial Intelligence can be used to find data in the most difficult of places. A patented approach to data discovery ensures efficient identification of sensitive data and minimal false positives. You can add data classifications to your existing 70+ data classifications that cover all popular PII/PHI data. A simplified discovery process allows you to schedule sample, full, and even incremental scans.

HEAVY.AI is a pioneer in accelerated analysis. The HEAVY.AI platform can be used by government and business to uncover insights in data that is beyond the reach of traditional analytics tools. The platform harnesses the huge parallelism of modern CPU/GPU hardware and is available both in the cloud or on-premise. HEAVY.AI was developed from research at Harvard and MIT Computer Science and Artificial Intelligence Laboratory. You can go beyond traditional BI and GIS and extract high-quality information from large datasets with no lag by leveraging modern GPU and CPU hardware. To get a complete picture of what, when and where, unify and explore large geospatial or time-series data sets. Combining interactive visual analytics, hardware accelerated SQL, advanced analytics & data sciences frameworks, you can find the opportunity and risk in your enterprise when it matters most.

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.

To make log data easily accessible and usable, it is important to have a single, unified layer of logging. However, existing tools fall short: legacy tools are not built for new cloud APIs and microservice-oriented architecture in mind and are not innovating quickly enough. Treasure Data created Fluentd to solve the problems of creating a unified log layer with a modular architecture and extensible plugin model. It also has a performance optimized engine. Fluentd Enterprise also addresses Enterprise requirements like Trusted Packaging. Security. Security.

ER/Studio Enterprise Team Edition allows data modelers and architects the ability to share data models and metadata throughout an enterprise. It offers a complete solution to enterprise architecture and data governance.

Greenplum Database®, an open-source data warehouse, is a fully featured, advanced, and fully functional data warehouse. It offers powerful and fast analytics on petabyte-scale data volumes. Greenplum Database is uniquely designed for big data analytics. It is powered by the most advanced cost-based query optimizer in the world, delivering high analytical query performance with large data volumes. The Apache 2 license is used to release Greenplum Database®. We would like to thank all of our community contributors. We are also open to new contributions. We encourage all contributions to the Greenplum Database community, no matter how small. Open-source, massively parallel data platform for machine learning, analytics, and AI. Rapidly create and deploy models to support complex applications in cybersecurity, predictive management, risk management, fraud detection, among other areas. The fully integrated, open-source analytics platform is now available.