Best Data Management Software for Apache HBase

OpenDQ is a zero-cost enterprise data quality, master and governance solution. OpenDQ is modularly built and can scale to meet your enterprise data management requirements. OpenDQ provides trusted data using a machine learning- and artificial intelligence-based framework. Comprehensive Data Quality Matching Profiling Data/Address Standardization Master Data Management 360 View of Customer Data Governance Business Glossary Meta Data Management

Our namesake product, Minitab Statistical Software, leads the way in data analysis with the power to visualize, analyze and harness your data to gain insights and solve your toughest challenges. Access trusted, proven and modern analytics combined with dynamic visualizations to empower you and your decisions. The latest version of Minitab Statistical Software includes access to Minitab on the cloud so you can analyze from anywhere, and Graph Builder, our new interactive tool to instantly create multiple graph options at once. Minitab offers modules for Predictive Analytics and Healthcare to boost your analytics even further. Available in 8 languages: English, Chinese, French, German, Japanese, Korean, Spanish, and Portuguese. For 50 years, Minitab has helped thousands of companies and institutions spot trends, solve problems, and discover valuable insights in their data through our comprehensive, best-in-class suite of data analysis and process improvement tools.

RazorSQL is a SQL query tool, a database browser, SQL editor and database administration tool for Windows and macOS, Mac OS X and Linux. RazorSQL can connect to more than 40 databases and has been tested on them. View database objects including schemas, tables and columns, primary and secondary keys, views and indexes, procedures and functions, and more. Visual tools to create and modify, describe, execute, and delete database objects like tables, views and indexes, stored procedure, functions, triggers, etc. Multi-tabular display of queries, with options for filtering and sorting, searching, etc. You can import data from many formats, including Excel spreadsheets, fixed-width files, and delimited files. It comes with a robust relational database (HSQLDB), which is ready to use straight out of the box.

IRI DarkShield uses several search techniques to find, and multiple data masking functions to de-identify, sensitive data in semi- and unstructured data sources enterprise-wide. You can use the search results to provide, remove, or fix PII simultaneously or separately to comply with GDPR data portability and erasure provisions. DarkShield jobs are configured, logged, and run from IRI Workbench or a restful RPC (web services) API to encrypt, redact, blur, etc., the PII it discovers in: * NoSQL & RDBs * PDFs * Parquet * JSON, XML & CSV * Excel & Word * BMP, DICOM, GIF, JPG & TIFF using pattern or dictionary matches, fuzzy search, named entity recognition, path filters, or image area bounding boxes. DarkShield search data can display in its own interactive dashboard, or in SIEM software analytic and visualization platforms like Datadog or Splunk ES. A Splunk Adaptive Response Framework or Phantom Playbook can also act on it. IRI DarkShield is a breakthrough in unstructured data hiding technology, speed, usability and affordability. DarkShield consolidates, multi-threads, the search, extraction and remediation of PII in multiple formats and folders on your network and in the cloud, on Windows, Linux, and macOS.

Hackolade is the pioneer for data modeling of NoSQL and multi-model databases, providing a comprehensive suite of data modeling tools for various NoSQL databases and APIs. Hackolade is the only data modeling tool for MongoDB, Neo4j, Cassandra, ArangoDB, BigQuery, Couchbase, Cosmos DB, Databricks, DocumentDB, DynamoDB, Elasticsearch, EventBridge Schema Registry, Glue Data Catalog, HBase, Hive, Firebase/Firestore, JanusGraph, MariaDB, MarkLogic, MySQL, Oracle, PostgreSQL, Redshift, ScyllaDB, Snowflake, SQL Server, Synapse, TinkerPop, YugabyteDB, etc. It also applies its visual design to Avro, JSON Schema, Parquet, Protobuf, Swagger and OpenAPI, and is rapidly adding new targets for its physical data modeling engine. The software is user-friendly and simple to use yet provides powerful visuals and graphic data modeling to smooth the onboarding of NoSQL technology. Its software tools help functional analysts, designers, architects, and DBAs involved with NoSQL technology achieve greater transparency and control, resulting in reduced development time, increased application quality, and lower execution risks across the enterprise.

Hue provides the best querying experience by combining the most intelligent autocomplete components and query editor. The tables and storage browses use your existing data catalog in a transparent way. Help users find the right data among thousands databases and document it themselves. Help users with their SQL queries, and use rich previews of links. Share directly from the editor in Slack. There are several apps, each specialized in one type of querying. Browsers are the first place to explore data sources. The editor excels at SQL queries. It has an intelligent autocomplete and risk alerts. Self-service troubleshooting is also available. Dashboards are primarily used to visualize indexed data, but they can also query SQL databases. The results of a search for specific cell values are highlighted. Hue has one of the most powerful SQL autocompletes on the planet to make your SQL editing experience as easy as possible.

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.

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.

The Stackable platform was built with flexibility and openness in mind. It offers a curated collection of open source data apps such as Apache Kafka Apache Druid Trino and Apache Spark. Stackable is different from other offerings that either push proprietary solutions or further vendor lock-in. All data apps are seamlessly integrated and can be added to or removed at any time. It runs anywhere, on-prem and in the cloud, based on Kubernetes. You only need stackablectl, a Kubernetes Cluster and stackablectl to run your stackable data platform. You will be able to work with your data within minutes. Configure your one line startup command here. Similar to kubectl stackablectl was designed to interface easily with the Stackable data Platform. Use the command-line utility to deploy and maintain stackable data apps in Kubernetes. You can create, delete and update components with stackablectl.

Cloud environments can be quickly set up for quick development, testing, and productivity for your IT staff and business users. Comprehensive data governance for business users will reduce the risks and cost of maintaining your data lakes. You can save money by providing consistent, timely, and clean information for your data lakes, big data projects, and data warehouses. Also, consolidate applications and retire outdated databases. Automatic schema propagation can be used to accelerate job generation, type-ahead searching, and backwards capabilities. All this while designing once and executing everywhere. With a cognitive design that recognizes patterns and suggests ways to use them, you can create data integration flows and enforce quality rules and data governance. You can improve visibility and information governance by creating authoritative views of information that are complete and authoritative.

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.

Titan is a graph database that can store and query graphs with hundreds of billions of edges and vertices distributed across a multi-machine cluster. Titan is a transactional database which can handle thousands of concurrent users performing complex graph traversals in real-time. For a growing user and data base, you can use linear and elastic scaling. Data replication and data distribution for performance and fault tolerance. Hot backups and high availability for multi-datacenters Support for ACID, eventual consistency and other storage backends. Support for Apache Cassandra and Apache HBase storage backends, as well as Oracle BerkeleyDB. Integration with big data platforms such as Apache Spark, Apache Giraph, and Apache Hadoop allows for global graph data analytics, reporting and ETL. Native integration with TinkerPop graph stack to support Gremlin's graph query language, Gremlin's graph server, and Gremlin apps.

Apache Ranger™, a framework that enables, monitors and manages comprehensive data security across Hadoop's platform, is called Apache Ranger. Ranger's goal is to provide complete security across the Apache Hadoop ecosystem. Apache YARN has made it possible to create a data lake architecture on Hadoop. Multi-tenant environments allow enterprises to run multiple workloads. Hadoop data security must evolve to support multiple use-cases for data access. It also provides a framework for central administration and monitoring of user access. All security-related tasks can be managed centrally through a UI or REST APIs using central security administration. Fine-grained authorization to perform a specific action or operation with a Hadoop component/tool. This is managed through a central admin tool. Standardize authorization methods across all Hadoop components. Enhanced support for different authorization methods, such as Role-based access control, etc.

The ever-on economy of today is creating data at an ever-increasing rate. It's important to be data-driven so that you can react quickly to new opportunities and stay ahead of your competitors. What if data provisioning and preparation could be simplified? What if you could share data insights across teams and perform database analysis more efficiently? Imagine if you could do this with a time saving of up to 40% Toad Intelligence Central, a server-based application, can be used in conjunction with Toad(r] Data Point. It transfers power back into your business at a cost-effective price. Secure, controlled access to SQL scripts, project artifacts and automation workflows can improve collaboration among Toad users. Advanced data connectivity allows you to easily abstract structured and unstructured data sources to create refreshable datasets that can be used by any Toad user.

Lyftrondata can help you build a governed lake, data warehouse or migrate from your old database to a modern cloud-based data warehouse. Lyftrondata makes it easy to create and manage all your data workloads from one platform. This includes automatically building your warehouse and pipeline. It's easy to share the data with ANSI SQL, BI/ML and analyze it instantly. You can increase the productivity of your data professionals while reducing your time to value. All data sets can be defined, categorized, and found in one place. These data sets can be shared with experts without coding and used to drive data-driven insights. This data sharing capability is ideal for companies who want to store their data once and share it with others. You can define a dataset, apply SQL transformations, or simply migrate your SQL data processing logic into any cloud data warehouse.

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.

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.

JanusGraph is an optimized graph database that can store and query graphs with hundreds of billions of edges and vertices distributed across a multi-machine cluster. JanusGraph is a project of The Linux Foundation and includes participants from Expero and Google, GRAKN.AI., Hortonworks. IBM, and Amazon. Linear and elastic scaling for growing data and users. Data replication and data distribution for performance and fault tolerance. Hot backups and high availability for multi-datacenters All functionality is completely free. There is no need to purchase commercial licenses. JanusGraph is completely open source under the Apache 2 License. JanusGraph is an open source transactional database that can handle thousands of concurrent users performing complex graph traversals in real-time. ACID and eventual consistency support. JanusGraph offers online transactional processing (OLTP) and global graph analytics (OLAP), through its Apache Spark integration.

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.

Powerful computing at your fingertips. Parallel computing and innovative algorithms are available. What are you waiting for?! This tool is designed to empower data scientists in the business. You can get the fastest computing through an all-inclusive platform that covers time-series. Shapelets offers analytical features such as forecasting, clustering and motif discovery, discords, and causality. To make Big Data analysis more efficient, you can run, extend, and integrate your own algorithms in the Shapelets platform. Shapelets can be integrated seamlessly with any data storage and collection solution. It can also be integrated with MS Office and any other visualization software to simplify and share your insights without needing any technical knowledge. Interactive visualizations are possible because our UI integrates with the server. Our modern interface allows you to make the most out of your metadata and present it in the various visual graphs available. Shapelets allows users in the oil, gas, or energy industry to analyze operational data in real-time.

Your business generates many data every day. This data can be invaluable if it is used correctly. This strategic information, when gathered together, opens up a vast array of possibilities. DigDash is a trusted partner in business intelligence. We can help you to exploit your data and improve your performance today. DigDash is there for you, from design to deployment, and all questions to development, in a close partnership. DigDash's DNA is flexible. We are committed to continuous improvement. Our software is easy to use at all levels. This software is a market leader. Our tool adapts to any business' operational vision. Your managers can make rational decisions by having real-time visibility of all your activities, including marketing, finance, sales, and HR.

As a leader in business, you must be able to trust your data, and be 100 percent certain that they are accurate, well-governed and compliant. Include all data from all sources and all locations without limitation. Understanding your data assets. Audit your project for quality, compliance and risk. Catalogue a complete inventory of data across all data types and sources, creating a shared understanding about your data assets. Conduct a fast, accurate, and affordable audit of your data. PCI, PII and PHI audits can be completed quickly, accurately and completely. No software to buy, as a service. Measure and audit the data quality and duplication of data across all your enterprise data assets - cloud-native or on-premises. Ensure compliance with global data privacy laws at scale. Discover, classify and audit privacy compliance. Monitor PII/PCI/PHI and automate DSAR processes.

Salesforce Data Cloud is an online data platform that allows businesses to collect, harmonize, and analyze data in real time. This creates a 360-degree customer profile which can be used across Salesforce's various applications, such as Marketing Cloud, Sales Cloud, and Service Cloud. It allows businesses collect, harmonize and analyze data in real-time, creating a 360° customer profile that can then be used across Salesforce's different applications, including Marketing Cloud, Sales Cloud and Service Cloud. This platform allows for faster, more personal customer interactions through the integration of data from online and off-line channels, such as CRM data, transactional information, and third-party sources. Salesforce Data Cloud offers advanced AI and analytics capabilities that help organizations gain deeper insight into customer behavior, and predict future needs. Salesforce Data Cloud helps improve customer experiences, target marketing, and data-driven decision making across departments by centralizing and refining the data.

Protect, Monitor, and Discover enterprise sensitive data across multiple platforms and environments. Automate your subject rights response and demonstrate regulatory compliance - all in one solution