Alternatives to Polars

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

StarTree Cloud is a fully-managed real-time analytics platform designed for OLAP at massive speed and scale for user-facing applications. Powered by Apache Pinot, StarTree Cloud provides enterprise-grade reliability and advanced capabilities such as tiered storage, scalable upserts, plus additional indexes and connectors. It integrates seamlessly with transactional databases and event streaming platforms, ingesting data at millions of events per second and indexing it for lightning-fast query responses. StarTree Cloud is available on your favorite public cloud or for private SaaS deployment. StarTree Cloud includes StarTree Data Manager, which allows you to ingest data from both real-time sources such as Amazon Kinesis, Apache Kafka, Apache Pulsar, or Redpanda, as well as batch data sources such as data warehouses like Snowflake, Delta Lake or Google BigQuery, or object stores like Amazon S3, Apache Flink, Apache Hadoop, or Apache Spark. StarTree ThirdEye is an add-on anomaly detection system running on top of StarTree Cloud that observes your business-critical metrics, alerting you and allowing you to perform root-cause analysis — all in real-time.

PySpark is a Python interface for Apache Spark. It allows you to create Spark applications using Python APIs. Additionally, it provides the PySpark shell that allows you to interactively analyze your data in a distributed environment. PySpark supports Spark's most popular features, including Spark SQL, DataFrame and Streaming. Spark SQL is a Spark module that allows structured data processing. It can be used as a distributed SQL query engine and a programming abstraction called DataFrame. The streaming feature in Apache Spark, which runs on top of Spark allows for powerful interactive and analytic applications across streaming and historical data. It also inherits Spark's ease-of-use and fault tolerance characteristics.

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.

A hybrid SQL-onHadoop engine that delivers advanced, security-rich data queries across enterprise big data sources including Hadoop object storage and data warehouses. IBM Db2 Big SQL, an enterprise-grade, hybrid ANSI compliant SQL-on-Hadoop engine that delivers massively parallel processing and advanced data query, is available. Db2 Big SQL allows you to connect to multiple sources, such as Hadoop HDFS and WebHDFS. RDMS, NoSQL database, object stores, and RDMS. You can benefit from low latency, high speed, data security, SQL compatibility and federation capabilities to perform complex and ad-hoc queries. Db2 Big SQL now comes in two versions. It can be integrated with Cloudera Data Platform or accessed as a cloud native service on the IBM Cloud Pak®. for Data platform. Access, analyze, and perform queries on real-time and batch data from multiple sources, including Hadoop, object stores, and data warehouses.

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

Qubole is an open, secure, and simple Data Lake Platform that enables machine learning, streaming, or ad-hoc analysis. Our platform offers end-to-end services to reduce the time and effort needed to run Data pipelines and Streaming Analytics workloads on any cloud. Qubole is the only platform that offers more flexibility and openness for data workloads, while also lowering cloud data lake costs up to 50%. Qubole provides faster access to trusted, secure and reliable datasets of structured and unstructured data. This is useful for Machine Learning and Analytics. Users can efficiently perform ETL, analytics, or AI/ML workloads in an end-to-end fashion using best-of-breed engines, multiple formats and libraries, as well as languages that are adapted to data volume and variety, SLAs, and organizational policies.

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

The Databricks Data Intelligence Platform enables your entire organization to utilize data and AI. It is built on a lakehouse that provides an open, unified platform for all data and governance. It's powered by a Data Intelligence Engine, which understands the uniqueness in your data. Data and AI companies will win in every industry. Databricks can help you achieve your data and AI goals faster and easier. Databricks combines the benefits of a lakehouse with generative AI to power a Data Intelligence Engine which understands the unique semantics in your data. The Databricks Platform can then optimize performance and manage infrastructure according to the unique needs of your business. The Data Intelligence Engine speaks your organization's native language, making it easy to search for and discover new data. It is just like asking a colleague a question.

Tabular is a table store that allows you to create an open table. It was created by the Apache Iceberg creators. Connect multiple computing frameworks and engines. Reduce query time and costs up to 50%. Centralize enforcement of RBAC policies. Connect any query engine, framework, or tool, including Athena BigQuery, Snowflake Databricks Trino Spark Python, Snowflake Redshift, Snowflake Databricks and Redshift. Smart compaction, data clustering and other automated services reduce storage costs by up to 50% and query times. Unify data access in the database or table. RBAC controls are easy to manage, enforce consistently, and audit. Centralize your security at the table. Tabular is easy-to-use and has RBAC, high-powered performance, and high ingestion under the hood. Tabular allows you to choose from multiple "best-of-breed" compute engines, based on their strengths. Assign privileges to the data warehouse database or table level.

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.

Starburst allows you to make better decisions by having quick access to all of your data. Your company has more data than ever, but your data teams are still waiting to analyze it. Starburst gives your data teams quick and accurate access to more data. Starburst Enterprise, a fully supported, production-tested, enterprise-grade distribution for open source Trino (formerly Presto®, SQL), is now available. It increases performance and security, while making it easy for you to deploy, connect, manage, and manage your Trino environment. Starburst allows your team to connect to any source of data, whether it's on-premise, in a cloud, or across a hybrid cloud environment. This allows them to use the analytics tools they already love and access data that lives anywhere.

Palo helps enterprises create the PB level MPP architecture data warehouse services in just a few minutes and import massive data from RDS BOS and BMR. Palo is able to perform multi-dimensional analysis of big data. Palo is compatible to mainstream BI tools. Data analysts can quickly gain insights by analyzing and displaying the data visually. It has an industry-leading MPP engine with column storage, intelligent indexes, and vector execution functions. It can also provide advanced analytics, window functions and in-library analytics. You can create a materialized table and change its structure without suspending service. It supports flexible data recovery.

Presto is an open-source distributed SQL query engine that allows interactive analytic queries against any data source, from gigabytes up to petabytes.

Perhaps you want to allow your clients to access their LIMS data via the internet. AIS labPortal makes it possible to do exactly that. Sample analyses can be emailed to customers, but not as paper copies. Clients can access their data using their unique login and security code from their computer. This is safer, faster, and more environmentally friendly than sending paper copies of sample analyses to customers. labPortal, a web-based portal, securely stores client's sample information and data in a cloud. Clients can access this data instantly from any computer, tablet, or smartphone. LabPortal's interface is an 'inbox' design that is simple and easy to use. It features an enhanced query engine, conditional highlight and Microsoft Excel export. The software includes an easy-to-use sample registration tool that allows users to preregister samples online. Transcribing data can be tedious and time-consuming.

Parallel query engine with symbol- and time-indexed data access. Extended SQL syntax with advanced filtering, aggregations and more. Consolidate all quotes, trades and snapshots in one place. Strategy backtesting using high-frequency data. Quantitative and market microstructure analysis. Granular transaction cost analysis and rollup report. Market surveillance and anomaly detection. Non-transparent ETF/ETN decomposition. FAST, SBE and proprietary protocols. Plain text protocol. Consolidated and direct feeds. Built-in latency monitoring tools. End-of-day archives. ETL from retail and institutional financial data platforms. Parallel SQL engine with syntax extensions. Advanced filtering via trading session, auction stage, and index composition. Optimized aggregates to OHLCV and VWAP calculations. Interactive SQL console with auto completion. API endpoint for programmatic integrtion. Scheduled SQL reporting via email, file, or web delivery. JDBC and ODBC drivers.

Rich interactive visualizations to identify patterns in user and company flows with full visibility of computation. In less than 10 lines of code, a small set of sequence operators can provide full expressivity and finely-grained control. A query engine that allows you to trade between query speed, precision and cost according your needs. Motif currently uses a custom-built DSL, called Sequence Operations Language. We believe it is more natural than SQL and more powerful that a drag-and drop interface. We built a custom algorithm to optimize sequence queries. We also trade off precision for query speed, which is not used in decision-making.

QuasarDB is Quasar's brain. It is a high-performance distributed, column-oriented, timeseries database management software system that delivers real-time data for petascale use cases. You can save up to 20X on your disk usage Quasardb compression and ingestion are unmatched. Feature extraction can be performed up to 10,000 times faster. QuasarDB is able to extract features from raw data in real-time thanks to a combination of a builtin map/reduce engine, an aggregate engine that leverages SIMD from modern processors, and stochastic indices that consume virtually no disk space.

Apache Hive™, a data warehouse software, facilitates the reading, writing and management of large datasets that are stored in distributed storage using SQL. Structure can be projected onto existing data. Hive provides a command line tool and a JDBC driver to allow users to connect to it. Apache Hive is an Apache Software Foundation open-source project. It was previously a subproject to Apache® Hadoop®, but it has now become a top-level project. We encourage you to read about the project and share your knowledge. To execute traditional SQL queries, you must use the MapReduce Java API. Hive provides the SQL abstraction needed to integrate SQL-like query (HiveQL), into the underlying Java. This is in addition to the Java API that implements queries.

PuppyGraph allows you to query multiple data stores in a single graph model. Graph databases can be expensive, require months of setup, and require a dedicated team. Traditional graph databases struggle to handle data beyond 100GB and can take hours to run queries with multiple hops. A separate graph database complicates architecture with fragile ETLs, and increases your total cost ownership (TCO). Connect to any data source, anywhere. Cross-cloud and cross region graph analytics. No ETLs are required, nor is data replication. PuppyGraph allows you to query data as a graph directly from your data lakes and warehouses. This eliminates the need for time-consuming ETL processes that are required with a traditional graph databases setup. No more data delays or failed ETL processes. PuppyGraph eliminates graph scaling issues by separating computation from storage.

SPListX for SharePoint allows you to export picture library contents, metadata, list items and associated file attachments to Windows File System. SharePoint site, libraries and folders can be exported to Windows File System. SPListX supports SharePoint 2019 / SharePoint 2016, SharePoint 2013 / SharePoint 2010, SharePoint 2007 / SharePoint 2003 & Office 365.

Amazon Athena allows you to easily analyze data in Amazon S3 with standard SQL. Athena is serverless so there is no infrastructure to maintain and you only pay for the queries you run. Athena is simple to use. Simply point to your data in Amazon S3 and define the schema. Then, you can query standard SQL. Most results are delivered in a matter of seconds. Athena makes it easy to prepare your data for analysis without the need for complicated ETL jobs. Anyone with SQL skills can quickly analyze large-scale data sets. Athena integrates with AWS Glue Data Catalog out-of-the box. This allows you to create a unified metadata repositorie across multiple services, crawl data sources and discover schemas. You can also populate your Catalog by adding new and modified partition and table definitions. Schema versioning is possible.

Amazon Timestream is a fast, scalable and serverless time series data service for IoT/operational applications. It makes it possible to store and analyze trillions per day up to 1000 times faster than traditional relational databases and at as low as 1/10th of the cost. Amazon Timestream helps you save time and money when managing the lifecycles of time series data. It stores recent data in memory and moves historical data to a cost-optimized storage tier according to user defined policies. Amazon Timestream's purpose-built query tool allows you to access and analyze both recent and historic data simultaneously, without having to specify in the query whether the data is in the in-memory tier or the cost-optimized. Amazon Timestream's built-in time series analytics functions allow you to identify trends and patterns within your data in real-time.

VeloDB, powered by Apache Doris is a modern database for real-time analytics at scale. In seconds, micro-batch data can be ingested using a push-based system. Storage engine with upserts, appends and pre-aggregations in real-time. Unmatched performance in real-time data service and interactive ad hoc queries. Not only structured data, but also semi-structured. Not only real-time analytics, but also batch processing. Not only run queries against internal data, but also work as an federated query engine to access external databases and data lakes. Distributed design to support linear scalability. Resource usage can be adjusted flexibly to meet workload requirements, whether on-premise or cloud deployment, separation or integration. Apache Doris is fully compatible and built on this open source software. Support MySQL functions, protocol, and SQL to allow easy integration with other tools.

StarRocks offers at least 300% more performance than other popular solutions, whether you're using a single or multiple tables. With a rich set connectors, you can ingest real-time data into StarRocks for the latest insights. A query engine that adapts your use cases. StarRocks allows you to scale your analytics easily without moving your data or rewriting SQL. StarRocks allows a rapid journey between data and insight. StarRocks is unmatched in performance and offers a unified OLAP system that covers the most common data analytics scenarios. StarRocks offers at least 300% faster performance than other popular solutions, whether you are working with one table or many. StarRocks' built-in memory-and-disk-based caching framework is specifically designed to minimize the I/O overhead of fetching data from external storage to accelerate query performance.

ClickHouse is an open-source OLAP database management software that is fast and easy to use. It is column-oriented, and can generate real-time analytical reports by using SQL queries. ClickHouse's performance is superior to comparable column-oriented database management software currently on the market. It processes hundreds of millions of rows to more than a million and tens if not thousands of gigabytes per second. ClickHouse makes use of all hardware available to process every query as quickly as possible. Peak processing speed for a single query is more than 2 Terabytes per Second (after decompression, only utilized columns). To reduce latency, reads in distributed setups are automatically balanced between healthy replicas. ClickHouse supports multimaster asynchronous replication, and can be deployed across multiple datacenters. Each node is equal, which prevents single points of failure.

Now that your data has been in motion, it is time to make sense. Stream processing allows you to extract instant insights from your data streams but it can be difficult to set up the infrastructure. Confluent created ksqlDB to support stream processing applications. Continuously processing streams of data from your business will make your data actionable. The intuitive syntax of ksqlDB allows you to quickly access and augment Kafka data, allowing development teams to create innovative customer experiences and meet data-driven operational requirements. ksqlDB is a single solution that allows you to collect streams of data, enrich them and then serve queries on new derived streams or tables. This means that there is less infrastructure to manage, scale, secure, and deploy. You can now focus on the important things -- innovation -- with fewer moving parts in your data architecture.

LlamaIndex, a "dataframework", is designed to help you create LLM apps. Connect semi-structured API data like Slack or Salesforce. LlamaIndex provides a flexible and simple data framework to connect custom data sources with large language models. LlamaIndex is a powerful tool to enhance your LLM applications. Connect your existing data formats and sources (APIs, PDFs, documents, SQL etc.). Use with a large-scale language model application. Store and index data for different uses. Integrate downstream vector stores and database providers. LlamaIndex is a query interface which accepts any input prompts over your data, and returns a knowledge augmented response. Connect unstructured data sources, such as PDFs, raw text files and images. Integrate structured data sources such as Excel, SQL etc. It provides ways to structure data (indices, charts) so that it can be used with LLMs.

You can create flat or relational databases with unlimited fields, tables, and rows. A custom report builder is included. Create custom reports by connecting to compatible ODBC databases. You can create your own databases. Here are some highlights: Filter tables instantly - Ultra simple graphical-user-interface - One-click table and data form creation - You can open up to 5 databases simultaneously Export your data to comma-separated files - Create custom reports to all your databases - A complete helpfile for creating database reports - You can print tables and queries directly from your data grid - Supports any SQL standard your ODBC compatible databases require For best performance and user experience, please install and run this database app with full administrator rights. Requirements: . 1024x768 Display Size . Windows 98 / XP / Windows 8 / Windows 10 - 32bit or 64bit No Java or DotNet are required. Green Energy Software. One step at a time, saving the planet

Don't let game, app, or device crashes stop you from having a great experience. Backtrace automates cross-platform exception management and cross-platform crash management so that you can focus on shipping. Cross-platform callstack, event aggregation, and monitoring. A single system can process errors from panics and core dumps, minidumps, as well as during runtime across your stack. Backtrace generates searchable, structured error reports from your data. Automated analysis reduces time to resolution by surfacing important signals which lead engineers to the crash root cause. Rich integrations into dashboards and notification systems mean that you don't have to worry about missing a detail. Backtrace's rich queries engine will help you answer the questions that are most important to you. A high-level overview of errors, prioritization and trends across all projects can be viewed. You can search through key data points as well as your own custom data for all errors.

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.

Timeplus is an easy-to-use, powerful and cost-effective platform for stream processing. All in one binary, easily deployable anywhere. We help data teams in organizations of any size and industry process streaming data and historical data quickly, intuitively and efficiently. Lightweight, one binary, no dependencies. Streaming analytics and historical functionality from end-to-end. 1/10 of the cost of comparable open source frameworks Transform real-time data from the market and transactions into real-time insight. Monitor financial data using append-only streams or key-value streams. Implement real-time feature pipelines using Timeplus. All infrastructure logs, metrics and traces are consolidated on one platform. In Timeplus we support a variety of data sources through our web console UI. You can also push data using REST API or create external streams, without copying data to Timeplus.

Deepnote is building the best data science notebook for teams. Connect your data, explore and analyze it within the notebook with real-time collaboration and versioning. Share links to your projects with other analysts and data scientists on your team, or present your polished, published notebooks to end users and stakeholders. All of this is done through a powerful, browser-based UI that runs in the cloud.

GeoSpock DB - The space-time analytics database - allows data fusion in the connected world. GeoSpockDB is a unique cloud-native database that can be used to query for real-world applications. It can combine multiple sources of Internet of Things data to unlock their full potential, while simultaneously reducing complexity, cost, and complexity. GeoSpock DB enables data fusion and efficient storage. It also allows you to run ANSI SQL query and connect to analytics tools using JDBC/ODBC connectors. Users can perform analysis and share insights with familiar toolsets. This includes support for common BI tools such as Tableau™, Amazon QuickSight™, and Microsoft Power BI™, as well as Data Science and Machine Learning environments (including Python Notebooks or Apache Spark). The database can be integrated with internal applications as well as web services, including compatibility with open-source visualisation libraries like Cesium.js and Kepler.

Schema-free SQL query engine for Hadoop, NoSQL, and Cloud Storage

Stata is a comprehensive, integrated software package that can handle all aspects of data science: data manipulation, visualization and statistics, as well as automated reporting. Stata is quick and accurate. The extensive graphical interface makes it easy to use, but is also fully programable. Stata's menus, dialogs and buttons give you the best of both worlds. All Stata's data management, statistical, and graphical features are easy to access by dragging and dropping or point-and-click. To quickly execute commands, you can use Stata's intuitive command syntax. You can log all actions and results, regardless of whether you use the menus or dialogs. This will ensure reproducibility and integrity in your analysis. Stata also offers complete command-line programming and programming capabilities, including a full matrix language. All the commands that Stata ships with are available to you, whether you want to create new Stata commands or script your analysis.

Your cloud data platform. Access to any data you need with unlimited scalability. All your data is available to you, with the near-infinite performance and concurrency required by your organization. You can seamlessly share and consume shared data across your organization to collaborate and solve your most difficult business problems. You can increase productivity and reduce time to value by collaborating with data professionals to quickly deliver integrated data solutions from any location in your organization. Our technology partners and system integrators can help you deploy Snowflake to your success, no matter if you are moving data into Snowflake.

With a single keystroke, switch between editor and command modes. Use the arrow keys to navigate between cells. All the Jupyter shortcuts are available. Fully interactive outputs are available right under the cell. Editing code cells is easy with smart code completion, quick error checking and quick fixes, and easy navigation. You can connect to remote JupyterHub or JupyterLab servers from the IDE. Interactively run Python scripts and arbitrary expressions in a Python Console. You can see the outputs and the state variables in real time. Split Python scripts into code cells using the #%% separator, and run them individually in a Jupyter notebook. Interactive controls allow you to browse DataFrames or visualizations in real time. All popular Python scientific libraries, including Plotly and Altair, ipywidgets and others, are supported.

Scale from 0 to millions of events every second. Arroyo is shipped as a single compact binary. Run locally on MacOS, Linux or Kubernetes for development and deploy to production using Docker or Kubernetes. Arroyo is an entirely new stream processing engine that was built from the ground-up to make real time easier than batch. Arroyo has been designed so that anyone with SQL knowledge can build reliable, efficient and correct streaming pipelines. Data scientists and engineers are able to build real-time dashboards, models, and applications from end-to-end without the need for a separate streaming expert team. SQL allows you to transform, filter, aggregate and join data streams with results that are sub-second. Your streaming pipelines should not page someone because Kubernetes rescheduled your pods. Arroyo can run in a modern, elastic cloud environment, from simple container runtimes such as Fargate, to large, distributed deployments using the Kubernetes logo.

Processing and storage of tabular datasets, e.g. CSV or Parquet files. Large result set transfer to client. Large client/server installations are required for central enterprise data warehousing. Multiple concurrent processes can be used to write to a single database. DuckDB is a relational database management software (RDBMS). It is a system to manage data stored in relational databases. A relation is basically a mathematical term for a particular table. Each table is a named collection. Each row in a table has the same number of named columns. Each column is of a particular data type. Schemas are used to store tables, and a collection can be accessed to access the entire database.

You can easily develop and execute massively parallel data processing and transformation programs in U-SQL and R. You don't need to maintain any infrastructure and can process data on-demand, scale instantly, or pay per job. Azure Data Lake Analytics makes it easy to process large data jobs in seconds. There are no servers, virtual machines or clusters to manage or tune. You can instantly scale your processing power in Azure Data Lake Analytics Units, (AU), to one to thousands per job. Only pay for the processing you use per job. Optimized data virtualization of relational sources, such as Azure SQL Database or Azure Synapse Analytics, allows you to access all your data. Your queries are automatically optimized by moving processing closer to the source data, which maximizes performance while minimising latency.

Forestpin Analytics runs complex mathematical tests on your data. It then provides you with easy to use analyses that highlight transactions that are not within the norm. These outliers could be due to fraud, mistakes or manipulation, business process improvements, and opportunity loss. No more queries. All you have to do is point, click, drag. You can easily add custom filters to your data in order to filter the most relevant data. Filter your data by date ranges, district and salesperson. You can also filter your data by product, product combinations, materials or sales outlet. The most relevant analyses for your data are automatically populated into custom dashboards. Paste data from spreadsheets into CSV files or copy it. Forestpin integrates with your existing ERP and finace systems, so you don’t have to worry too much about the implications.

Azure Databricks allows you to unlock insights from all your data, build artificial intelligence (AI), solutions, and autoscale your Apache Spark™. You can also collaborate on shared projects with other people in an interactive workspace. Azure Databricks supports Python and Scala, R and Java, as well data science frameworks such as TensorFlow, PyTorch and scikit-learn. Azure Databricks offers the latest version of Apache Spark and allows seamless integration with open-source libraries. You can quickly spin up clusters and build in an Apache Spark environment that is fully managed and available worldwide. Clusters can be set up, configured, fine-tuned, and monitored to ensure performance and reliability. To reduce total cost of ownership (TCO), take advantage of autoscaling or auto-termination.

Customers can deploy Hadoop clusters in any size using Oracle Big Data Service. VM shapes range from 1 OCPU up to a dedicated bare-metal environment. Customers can choose between high-performance block storage or cost-effective block store, and can grow and shrink their clusters. Create Hadoop-based data lakes quickly to expand or complement customer data warehouses and ensure that all data can be accessed and managed efficiently. The included notebook supports R, Python, and SQL. Data scientists can query, visualize, and transform data to build machine learning models. Transfer customer-managed Hadoop clusters from a managed cloud-based service to improve resource utilization and reduce management costs.

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.

Visokio creates Omniscope Evo, a complete and extensible BI tool for data processing, analysis, and reporting. Smart experience on any device. You can start with any data, any format, load, edit, combine, transform it while visually exploring it. You can extract insights through ML algorithms and automate your data workflows. Omniscope is a powerful BI tool that can be used on any device. It also has a responsive UX and is mobile-friendly. You can also augment data workflows using Python / R scripts or enhance reports with any JS visualisation. Omniscope is the complete solution for data managers, scientists, analysts, and data managers. It can be used to visualize data, analyze data, and visualise it.

The RAPIDS software library, which is built on CUDAX AI, allows you to run end-to-end data science pipelines and analytics entirely on GPUs. It uses NVIDIA®, CUDA®, primitives for low level compute optimization. However, it exposes GPU parallelism through Python interfaces and high-bandwidth memories speed through user-friendly Python interfaces. RAPIDS also focuses its attention on data preparation tasks that are common for data science and analytics. This includes a familiar DataFrame API, which integrates with a variety machine learning algorithms for pipeline accelerations without having to pay serialization fees. RAPIDS supports multi-node, multiple-GPU deployments. This allows for greatly accelerated processing and training with larger datasets. You can accelerate your Python data science toolchain by making minimal code changes and learning no new tools. Machine learning models can be improved by being more accurate and deploying them faster.

*GraphDB allows the creation of large knowledge graphs by linking diverse data and indexing it for semantic search. * GraphDB is a robust and efficient graph database that supports RDF and SPARQL. The GraphDB database supports a highly accessible replication cluster. This has been demonstrated in a variety of enterprise use cases that required resilience for data loading and query answering. Visit the GraphDB product page for a quick overview and a link to download the latest releases. GraphDB uses RDF4J to store and query data. It also supports a wide range of query languages (e.g. SPARQL and SeRQL), and RDF syntaxes such as RDF/XML and Turtle.

Hopsworks is an open source Enterprise platform that allows you to develop and operate Machine Learning (ML), pipelines at scale. It is built around the first Feature Store for ML in the industry. You can quickly move from data exploration and model building in Python with Jupyter notebooks. Conda is all you need to run production-quality end-to-end ML pipes. Hopsworks can access data from any datasources you choose. They can be in the cloud, on premise, IoT networks or from your Industry 4.0-solution. You can deploy on-premises using your hardware or your preferred cloud provider. Hopsworks will offer the same user experience in cloud deployments or the most secure air-gapped deployments.

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