Alternatives to Apache Hive

ANSI SQL allows you to analyze petabytes worth of data at lightning-fast speeds with no operational overhead. Analytics at scale with 26%-34% less three-year TCO than cloud-based data warehouse alternatives. You can unleash your insights with a trusted platform that is more secure and scales with you. Multi-cloud analytics solutions that allow you to gain insights from all types of data. You can query streaming data in real-time and get the most current information about all your business processes. Machine learning is built-in and allows you to predict business outcomes quickly without having to move data. With just a few clicks, you can securely access and share the analytical insights within your organization. Easy creation of stunning dashboards and reports using popular business intelligence tools right out of the box. BigQuery's strong security, governance, and reliability controls ensure high availability and a 99.9% uptime SLA. Encrypt your data by default and with customer-managed encryption keys

Composable is an enterprise-grade DataOps platform designed for business users who want to build data-driven products and create data intelligence solutions. It can be used to design data-driven products that leverage disparate data sources, live streams, and event data, regardless of their format or structure. Composable offers a user-friendly, intuitive dataflow visual editor, built-in services that facilitate data engineering, as well as a composable architecture which allows abstraction and integration of any analytical or software approach. It is the best integrated development environment for discovering, managing, transforming, and analysing enterprise data.

It shouldn't take long to analyze data from your business solutions. Zuar Runner allows you to automate your ELT/ETL processes, and have data flow from hundreds of sources into one destination. Zuar Runner can manage everything: transport, warehouse, transformation, model, reporting, and monitoring. Our experts will make sure your deployment goes smoothly and quickly.

Hudi is a rich platform for building streaming data lakes using incremental data pipelines on a self managing database layer. It can also be optimized for regular batch processing and lake engines. Hudi keeps a timeline of all actions on the table at different times. This allows for instantaneous views and efficient retrieval of data in the order they were received. The following components make up a Hudi instant. Hudi provides efficient upserts by mapping a given Hoodie key consistently with a file ID, via an indexing mechanism. Once a record is written to a file, the mapping between record key/file group/file ID never changes. The mapped file group includes all versions of a group record.

Apache HBase™, is used when you need random, real-time read/write access for your Big Data. This project aims to host very large tables, billions of rows and X million columns, on top of clusters of commodity hardware.

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.

Apache Kylin™, an open-source distributed Analytical Data Warehouse (Big Data), was created to provide OLAP (Online Analytical Processing), in this big data era. Kylin can query at near constant speed regardless of increasing data volumes by renovating the multi-dimensional cube, precalculation technology on Hadoop or Spark, and thereby achieving almost constant query speed. Kylin reduces query latency from minutes down to a fraction of a second, bringing online analytics back into big data. Kylin can analyze more than 10+ billion rows in less time than a second. No more waiting for reports to make critical decisions. Kylin connects Hadoop data to BI tools such as Tableau, PowerBI/Excel and MSTR. This makes Hadoop BI faster than ever. Kylin is an Analytical Data Warehouse and offers ANSI SQL on Hadoop/Spark. It also supports most ANSI SQL queries functions. Because of the low resource consumption for each query, Kylin can support thousands upon thousands of interactive queries simultaneously.

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.

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.

Open core, delivering hybrid cloud and multi-cloud integration Data Fusion is built with open source project CDAP. This open core allows users to easily port data from their projects. Cloud Data Fusion users can break down silos and get insights that were previously unavailable thanks to CDAP's integration with both on-premises as well as public cloud platforms. Integrated with Google's industry-leading Big Data Tools Data Fusion's integration to Google Cloud simplifies data security, and ensures that data is instantly available for analysis. Cloud Data Fusion integration makes it easy to develop and iterate on data lakes with Cloud Storage and Dataproc.

Apache Sentry™, a system to enforce fine-grained role-based authorizations to data and metadata stored on Hadoop clusters, is called Apache Sentry. Apache Sentry successfully graduated from the Incubator on March 16, 2016 and is now a Top Level Apache project. Apache Sentry is a role-based, granular authorization module for Hadoop. Sentry allows you to set and enforce specific privilege levels on data for authenticated users on a Hadoop cluster. Sentry works with Apache Hive Metastore/HCatalog and Apache Solr. Sentry is a pluggable authorization engine that can be used with Hadoop components. It allows you to define authorization rules that validate access requests to Hadoop resources by users or applications. Sentry is modular and can support authorization for a wide range of data models in Hadoop.

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.

QuerySurge is the smart Data Testing solution that automates the data validation and ETL testing of Big Data, Data Warehouses, Business Intelligence Reports and Enterprise Applications with full DevOps functionality for continuous testing. Use Cases - Data Warehouse & ETL Testing - Big Data (Hadoop & NoSQL) Testing - DevOps for Data / Continuous Testing - Data Migration Testing - BI Report Testing - Enterprise Application/ERP Testing Features Supported Technologies - 200+ data stores are supported QuerySurge Projects - multi-project support Data Analytics Dashboard - provides insight into your data Query Wizard - no programming required Design Library - take total control of your custom test desig BI Tester - automated business report testing Scheduling - run now, periodically or at a set time Run Dashboard - analyze test runs in real-time Reports - 100s of reports API - full RESTful API DevOps for Data - integrates into your CI/CD pipeline Test Management Integration QuerySurge will help you: - Continuously detect data issues in the delivery pipeline - Dramatically increase data validation coverage - Leverage analytics to optimize your critical data - Improve your data quality at speed

BigBI allows data specialists to create their own powerful Big Data pipelines interactively and efficiently, without coding! BigBI unleashes Apache Spark's power, enabling: Scalable processing of Big Data (upto 100X faster). Integration of traditional data (SQL and batch files) with new data Sources include semi-structured data (JSON, NoSQL DBs and Hadoop) as well as unstructured data (text, audio, video). Integration of streaming data and cloud data, AI/ML graphs & graphs

Apache Derby, an Apache DB Subproject, is an open-source relational database that's entirely written in Java. It is available under the Apache License Version 2.0. Derby is small in size, taking up 3.5 megabytes to run the base engine and embed JDBC driver. Derby has an embedded JDBC driver which allows you to embed Derby in any Java-based application. Derby supports the client/server mode with Derby Network Client JDBC driver, and Derby Network Server.

InDriver: The Multifunctional Automation engine powered by JavaScript allows for simultaneous task execution. InStudio: GUI application for remote InDriver Configuration across multiple computers. With minimal JS code, and a few mouse clicks, you can easily transform setups into tailored solution. Key Applications Data Automation and Integration Engine Conduct Extract-Transform-Load (ETL) operations effortlessly. Access to RESTful API Resources is streamlined, with simplified request definition, interval settings, JSON data processing and database logins. Industrial Automation Engine Interfacing seamless with PLCs and sensors. Create control algorithms, read/write data and process data to SCADA, MES and other systems. Database Automation Schedule queries to run at specific intervals or on specific events. This will ensure continuous automation.

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

Many people use Excel, VBA/Python or SQL queries to prepare data. EasyMorph is a purpose built application that has more than 150 built in actions that allow for quick and visual data transformations and automation without the need to code. EasyMorph makes it easy to get rid of complicated scripts and tedious spreadsheets and boosts your productivity. Access data from spreadsheets, emails, email attachments, text files and remote folders. SharePoint, and web (REST APIs) without programming. Visual queries and tools can be used to filter and extract the data you need, without having to ask the IT guys. Automate routine operations using files, spreadsheets websites and emails, without having to write a single line code. One button click can replace repetitive tasks.

The modern data workspace. All your data assets, from data tables to reports, will be instantly discoverable. The combination of powerful search algorithms and easy browsing makes it easy to find the right asset. Atlan automatically generates data quality profiles that make it easy to detect bad data. We have you covered, from automatic variable type detection and frequency distribution to missing values or outlier detection. Atlan takes the hassle out of managing and governing your data ecosystem. Atlan's bots analyze SQL query history to automatically construct data lineage. They also auto-detect PII information. This allows you to create dynamic access policies and best-in-class governance. Our Excel-like query builder allows anyone to query multiple data lakes, warehouses, and DBs. Native integrations with tools such as Tableau and Jupyter make data collaboration possible.

The elegant import button for web apps Drop-in data importer that can be implemented in hours instead of weeks You can give your users the import experience they have always wanted, but never had the time to create. Flatfile's JavaScript configurator lets you set a target model to validate data, allowing users match incoming file information. Flatfile is able to learn over time how data should look, which saves time and makes the process more efficient for customers. Flatfile's validation tools give you complete control over the way data is formatted. You can ensure that imported data is clean and ready for use. You can try the import flow using our file in a custom configuration. To view import analytics, complete the demo in admin dashboard. Flatfile automatically converts to the language of your customer. Advanced functions for data validation and transformation in-line. Data can be uploaded via XLS, CSV, or manually copied from the clipboard.

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.

AWS Data Pipeline, a web service, allows you to reliably process and transfer data between different AWS compute- and storage services as well as on premises data sources at specific intervals. AWS Data Pipeline allows you to access your data wherever it is stored, transform it and process it at scale, then transfer it to AWS services like Amazon S3, Amazon RDS and Amazon DynamoDB. AWS Data Pipeline makes it easy to create complex data processing workloads that can be fault-tolerant, repeatable, high-availability, and reliable. You don't need to worry about resource availability, managing intertask dependencies, retrying transient errors or timeouts in individual task, or creating a fail notification system. AWS Data Pipeline allows you to move and process data previously stored in on-premises silos.

HerdDB is a Java-based SQL distributed database. It can be embedded in any Java Virtual Machine. It is optimized for quick "writes" as well as primary key read/update access patterns. HerdDB can manage hundreds of tables. It is easy to add or remove hosts, and to reconfigure tablesspaces to distribute the load across multiple systems. HerdDB uses Apache Zookeeper and Apache Bookkeeper, to create a fully replicated, shared no-one architecture that is fault-free. HerdDB is very similar at the low level to a key-value NoSQL data base. An SQL abstraction layer and JDBC driver support allow users to leverage their existing knowledge and port existing applications into HerdDB. EmailSuccess is a powerful MTA (Mail Transfer Agent) that delivers millions of emails per hour to all over the globe. It was developed by Diennea.

In a developer-friendly visual editor, you can design, debug, run, and troubleshoot data jobflows and data transformations. You can orchestrate data tasks that require a specific sequence and organize multiple systems using the transparency of visual workflows. Easy deployment of data workloads into an enterprise runtime environment. Cloud or on-premise. Data can be made available to applications, people, and storage through a single platform. You can manage all your data workloads and related processes from one platform. No task is too difficult. CloverDX was built on years of experience in large enterprise projects. Open architecture that is user-friendly and flexible allows you to package and hide complexity for developers. You can manage the entire lifecycle for a data pipeline, from design, deployment, evolution, and testing. Our in-house customer success teams will help you get things done quickly.

It takes a lot time and manual coding to build and manage a fully documented data project. But not anymore. We can show you how we can help transform data faster. Column-aware architecture allows for reusable data patterns and change management at large scale. For safer and more predictable data operations, visibility is key to change management and impact analysis. Coalesce offers curated packages that use best-practice templates to generate native-SQL for Snowflake™ automatically. Have a unique need? Templates are easily customizable. Coalesce makes it easy to navigate your data pipeline. Every screen and button are designed to give you easy access to all the information you need. Your data team has greater control over every project. You can instantly see audit and project history, as well as code comparisons side-by-side. Automatically, lineage at the table- and column-levels is provided and kept up-to-date.

Self-service operational analytics and ready to-run reports across core processes empower your business to answer the most important questions. Imagine if you could get a better understanding of your organization's activities. You could not only report on the events but also react immediately to insights from your supply chain, finance, and manufacturing processes. You can adapt to the changing business landscape by changing the way you react. Magnitude Angles enables you to uncover hidden insights in your SAP ERP system or Oracle ERP system. It also streamlines the data analysis process. Traditional BI tools can only understand rows, columns, and tables, but not orders, cash, or materials. Angles is built upon a context-aware and process-rich business model that transforms complex ERP data architectures into self service business analytics. This allows data to be closer to decision making and helps turn insight into action.

Cloud-Native ETL tool. You can load and transform data to your cloud data warehouse in minutes. We have redesigned the traditional ETL process to create a solution for data integration in the cloud. Our solution makes use of the cloud's near-infinite storage capacity, which means that your projects have near-infinite scaling. We reduce the complexity of moving large amounts data by working in the cloud. In just fifteen minutes, you can process a billion rows and go live in five minutes. Modern businesses need to harness their data to gain greater business insight. Matillion can help you take your data journey to the next level by migrating, extracting, and transforming your data in cloud. This will allow you to gain new insights as well as make better business decisions.

The Apache Xalan Project creates and maintains libraries that can transform XML documents using XSLT standards stylesheets. To implement the XSLT libraries, our subprojects use Java and C++ programming languages. April 2014 saw the release of Xalan Java 2.7.2. Download the latest Xalan Java 2.7.2 release to get started with your development. You can find the current work in progress in the subversion repository. This release addresses a security problem that was reported against version 2.7.1. The Apache Archives still has the old Xalan J 2.7.1 distributions. This is a mature project. There has been some discussion about XPath-2 support. We would appreciate your support for this major overhaul of the library. You can follow the progress and contribute to the Java developers and Java users mail lists.

Apache Doris is an advanced data warehouse for real time analytics. It delivers lightning fast analytics on real-time, large-scale data. Ingestion of micro-batch data and streaming data within a second. Storage engine with upserts, appends and pre-aggregations in real-time. Optimize for high-concurrency, high-throughput queries using columnar storage engine, cost-based query optimizer, and vectorized execution engine. Federated querying for data lakes like Hive, Iceberg, and Hudi and databases like MySQL and PostgreSQL. Compound data types, such as Arrays, Maps and JSON. Variant data types to support auto datatype inference for JSON data. NGram bloomfilter for text search. Distributed design for linear scaling. Workload isolation, tiered storage and efficient resource management. Supports shared-nothing as well as the separation of storage from compute.

Apache CouchDB™, allows you to access your data wherever you need it. The Couch Replication Protocol can be used in a variety products and projects that span all possible computing environments, from global distributed server-clusters to mobile phones to web browsers.

Cloud-based applications for your company. Cloud Business Intelligence to help with marketing, sales, and operations. 100% Amazon Web Services solutions. No servers needed, no prepayments. Collect AWS Lambda workers. AWS Scheduled Events. Tokens management. Transform. DynamoDB as zero-like super reliable no-SQL storage. You can store raw data and trigger transformations. AWS Lambda Serverless ETL logic. Store. DynamoDB streams AWS S3 + cSV files as lightweight, cheap objects storage. It integrates well with big data HDFS distributed storage. Explore. AWS Athena is an open-source Hadoop Hive-based solution from the big data ecosystem. AWS S3 can be used as a native datasource to access CSV files and file SQL-like queries. AWS Quicksights is available for BI dashboards. Athena + S3 can be used as a datasource. Mobile and web Quicksight clients. Quicksight allows drill-down, filters, and many other features.

Power Query makes it easy to connect, extract and transform data from a variety of sources. Power Query is a data preparation and transformation engine. Power Query includes a graphical interface to retrieve data from sources, and a Power Query Editor to apply transformations. The destination where the data will be stored is determined by where Power Query was installed. Power Query allows you to perform the extraction, transform, load (ETL), processing of data. Microsoft's Data Connectivity and Data Preparation technology allows you to seamlessly access data from hundreds of sources and modify it to your requirements. It is easy to use and engage with, and requires no code. Power Query supports hundreds data sources with built in connectors and generic interfaces (such REST APIs and ODBC, OLE and DB) as well as the Power Query SDK for creating your own connectors.

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.

Equalum's continuous data integr & streaming platform is unique in that it natively supports real time, batch, and ETL use case under one platform. There is no coding required. You can move to real time with a fully orchestrated, drag and drop, no-code UI. You will experience rapid deployment, powerful transformations and scalable streaming data pipes in minutes. Multi-modal, robust and scalable CDC enables real-time streaming and data replicating. No matter what source, the CDC is tuned for best-in class performance. The power of open-source big dataset frameworks without the hassle. Equalum leverages the Scalability of Open-Source Data Frameworks like Apache Spark and Kafka in its Platform engine to dramatically improve streaming and batch data processing performance. This best-in-class infrastructure allows organizations to increase data volumes, improve performance, and minimize system impact.

Apache Pulsar, a cloud-native distributed messaging and streaming platform, was originally created by Yahoo! It is now a top-level Apache Software Foundation Project. It is easy to deploy, lightweight, and can be used by developers. You don't need to create your own stream processing engine. Yahoo! Production. Yahoo! has been in production for more than 5 years with millions of messages per minute across millions of topics. As a multi-tenant system, it was built from the ground up. Supports isolation, authentication authorization, authorization, and quotas. Configurable replication between data centres across multiple geographical regions. Persistent message storage using Apache BookKeeper. IO-level isolation between read and write operations. Rest admin API for provisioning and administration, tools, and monitoring.

Learning code shouldn't stop you from finding mission-critical information. Datawisp replaces code with visual blocks. Simply pick a data source, transform the data, and choose an output type. The visual query builder allows you to work with multiple data sets and format the result as a table or chart. Datawisp is a no-code query generator that allows you to work with one or more data sets and format the results as a table or chart. This will help your team work with data effectively and help you drive your business forward. Datawisp sheets can be easily shared across teams, making it simple to collaborate with others in real-time. Our API allows you to access analysis from third-party websites and apps. You can create an in-game leaderboard and export wallet addresses to make a whitelist.

CData Sync is a universal database pipeline that automates continuous replication between hundreds SaaS applications & cloud-based data sources. It also supports any major data warehouse or database, whether it's on-premise or cloud. Replicate data from hundreds cloud data sources to popular databases destinations such as SQL Server and Redshift, S3, Snowflake and BigQuery. It is simple to set up replication: log in, select the data tables you wish to replicate, then select a replication period. It's done. CData Sync extracts data iteratively. It has minimal impact on operational systems. CData Sync only queries and updates data that has been updated or added since the last update. CData Sync allows for maximum flexibility in partial and full replication scenarios. It ensures that critical data is safely stored in your database of choice. Get a 30-day trial of the Sync app for free or request more information at www.cdata.com/sync

Alooma allows data teams visibility and control. It connects data from all your data silos into BigQuery in real-time. You can set up and flow data in minutes. Or, you can customize, enrich, or transform data before it hits the data warehouse. Never lose an event. Alooma's safety nets make it easy to handle errors without affecting your pipeline. Alooma infrastructure can handle any number of data sources, low or high volume.

Your data models can be created, edited, and organized. You don't need another data tool to manage your data models. Weld allows you to create and manage them. It is packed with features that make it easy to create your data models: smart autocomplete, code folding and error highlighting, audit logs and version control, collaboration, and version control. We use the same text editor that VS Code - it is fast, powerful, and easy to read. Your queries are organized in a searchable and easily accessible library. Audit logs allow you to see when and by whom the query was last updated. Weld Model allows you to materialize models as views, tables, incremental tables, and views. You can also create custom materializations of your design. With the help of a dedicated team, you can manage all your data operations from one platform.

Hive increases productivity among team members. Hive is a powerful collaboration and project management platform that offers a multitude of features in one comprehensive solution. The platform includes transparent project management tools, team communication and file storage and sharing. Time tracking and app integrations are also available.

A fast extract step can be a critical component of: database archive and replication database reorgs and migrations data warehouse ETL, ELT, and ODS operations offline reporting and bulk data protection IRI Fast Extract (FACT™) is a parallel unload utility for very large database (VLDB) tables in: Oracle DB2 UDB MS SQL Server Sybase MySQL Greenplum Teradata Altibase Tibero FACT uses simple job scripts (supported in a familiar Eclipse GUI) to rapidly create portable flat files. FACT's speed comes from native connection protocols and proprietary split query logic that unloads billions of rows in minutes. Although FACT is a standalone, application-independent utility, it can also work nicely with other programs and platforms. For example, FACT optionally creates metadata for data definition files (.DDF) that IRI CoSort and its compatible data management and protection tools can use to manipulate the flat files. FACT also automatically creates database load utility configuration files for the same source. FACT is also an optional, seamlessly integrated component in the IRI Voracity ETL and data management platform. The automatic metadata creation -- and coexistence of other IRI software in the same IDE --

Data analysts can now gain insights faster and without any barriers. Intelligent data stack automation, gain insights from your data 10x quicker with AI. Intelligence for the modern data-stack has arrived, a technology that was developed at Stanford's AI lab and is now available to enterprises. Use natural language to extract value from your messy data, complex and siloed in minutes. Tell your data what you want and it will generate code to execute. Automate complex data tasks in a way that is specific to your company and not covered by templated solutions. Automate data-intensive workflows using the modern data stack. Discover insights in minutes and not months. Uniquely designed and tuned to your organization's requirements. Snowflake, Databricks Redshift, BigQuery and more are integrated with dbt.

Ascend provides data teams with a unified platform that allows them to ingest and transform their data and create and manage their analytics engineering and data engineering workloads. Ascend is supported by DataAware intelligence. Ascend works in the background to ensure data integrity and optimize data workloads, which can reduce maintenance time by up to 90%. Ascend's multilingual flex-code interface allows you to use SQL, Java, Scala, and Python interchangeably. Quickly view data lineage and data profiles, job logs, system health, system health, and other important workload metrics at a glance. Ascend provides native connections to a growing number of data sources using our Flex-Code data connectors.

Panoply makes it easy to store, sync and access all your business information in the cloud. With built-in integrations to all major CRMs and file systems, building a single source of truth for your data has never been easier. Panoply is quick to set up and requires no ongoing maintenance. It also offers award-winning support, and a plan to fit any need.

Indigo Scape is an advanced Data Reporting & Document Generation System that uses HTML, XML and XSLT to create highly compatible, richly-structured business reports and documents. Our advanced technology and reusable report system are the best in data reporting. Indigo DRS is a completely unique tool that can query in XQuery and SQL, and use data from multiple sources simultaneously, making it the best choice for complex business, financial and engineering reporting. You can be sure of the best reporting capabilities with advanced reporting features, unmatched functionality, and seamless integration of this powerful software technology to your business.

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.

RestApp is a No Code Data Activation Platform that provides anyone with an all-in one solution to connect, model and sync any data using his favorite tools. RestApp allows Data & Ops teams activate data in minutes using No-Code by: Connecting to your favorite databases and business apps Drag-and-drop SQL, NoSQL, and Python functions to model your data and then create and share queries with your colleagues - Automatically sync your data with your tools RestApp makes it easy to use our templates to: - Computing your main financial KPIs: churn rate, MRR, ARR, ACV, ARPU, LVT - Calculating your customers' lead scoring - Generate automatic cohort analyses

Apache Lucene™, an open-source search engine, is developed by the Apache Lucene™. The project releases Lucene™, a core search library. It also includes PyLucene, a python binding to Lucene. Lucene Core is a Java library providing powerful indexing and search features, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities. The PyLucene sub-project provides Python bindings to Lucene Core. The Apache Software Foundation supports the Apache community of open-source projects. Apache Lucene is available under a commercially-friendly Apache Software license. Apache Lucene is the benchmark for search and indexing performance. Lucene is the search engine for both Apache Solr™, and Elasticsearch™. Our core algorithms and the Solr search server power applications all over the globe, from mobile devices to websites like Twitter, Apple, Wikipedia, and Google. Apache Lucene's goal is to provide world-class search capabilities.

No per-connection charges for setting up all the automated connections that you need. No per-connection fees for all the automated connections that you need. No technical expertise is required to set up and scale your cloud data warehouse or processing operations. Conversionomics allows you to make mistakes and ask hard questions about your data. You have the power to do whatever you want with your data. Conversionomics creates complex SQL to combine source data with lookups and table relationships. You can use preset joins and common SQL, or create your own SQL to customize your query. Conversionomics is a data aggregation tool with a simple interface that makes it quick and easy to create data API sources. You can create interactive dashboards and reports from these sources using our templates and your favorite data visualization tools.

Apache Accumulo allows users to store and manage large data sets across a cluster. Accumulo uses Apache Hadoop HDFS to store its data, and Apache ZooKeeper to reach consensus. Accumulo is used by many users, but there are also open-source projects that use it as their underlying store. Take the Accumulo tour to learn more, and then run the Accumulo sample code. If you have any questions, please don't hesitate to contact us. Accumulo offers a programming mechanism called Iterators that allows you to modify key/value pair at different points in the data management process. Each Accumulo key/value pair is assigned a security label that limits the query results based on user authorizations. Accumulo can be run on a cluster that uses one or more HDFS instances. As Accumulo's data grows, nodes can be added and removed.