Alternatives to IBM Data Refinery

BigQuery is a serverless, multicloud data warehouse that makes working with all types of data effortless, allowing you to focus on extracting valuable business insights quickly. As a central component of Google’s data cloud, it streamlines data integration, enables cost-effective and secure scaling of analytics, and offers built-in business intelligence for sharing detailed data insights. With a simple SQL interface, it also supports training and deploying machine learning models, helping to foster data-driven decision-making across your organization. Its robust performance ensures that businesses can handle increasing data volumes with minimal effort, scaling to meet the needs of growing enterprises. Gemini within BigQuery brings AI-powered tools that enhance collaboration and productivity, such as code recommendations, visual data preparation, and intelligent suggestions aimed at improving efficiency and lowering costs. The platform offers an all-in-one environment with SQL, a notebook, and a natural language-based canvas interface, catering to data professionals of all skill levels. This cohesive workspace simplifies the entire analytics journey, enabling teams to work faster and more efficiently.

Domo puts data to work for everyone so they can multiply their impact on the business. Underpinned by a secure data foundation, our cloud-native data experience platform makes data visible and actionable with user-friendly dashboards and apps. Domo helps companies optimize critical business processes at scale and in record time to spark bold curiosity that powers exponential business results.

IBM® SPSS® Statistics software is used by a variety of customers to solve industry-specific business issues to drive quality decision-making. The IBM® SPSS® software platform offers advanced statistical analysis, a vast library of machine learning algorithms, text analysis, open-source extensibility, integration with big data and seamless deployment into applications. Its ease of use, flexibility and scalability make SPSS accessible to users of all skill levels. What’s more, it’s suitable for projects of all sizes and levels of complexity, and can help you find new opportunities, improve efficiency and minimize risk.

Rivery’s ETL platform consolidates, transforms, and manages all of a company’s internal and external data sources in the cloud. Key Features: Pre-built Data Models: Rivery comes with an extensive library of pre-built data models that enable data teams to instantly create powerful data pipelines. Fully managed: A no-code, auto-scalable, and hassle-free platform. Rivery takes care of the back end, allowing teams to spend time on mission-critical priorities rather than maintenance. Multiple Environments: Rivery enables teams to construct and clone custom environments for specific teams or projects. Reverse ETL: Allows companies to automatically send data from cloud warehouses to business applications, marketing clouds, CPD’s, and more.

The most accurate, complete, and timely data provides the best insight. Minitab Connect empowers data users across the enterprise with self service tools to transform diverse data into a network of data pipelines that feed analytics initiatives, foster collaboration and foster organizational-wide collaboration. Users can seamlessly combine and explore data from various sources, including databases, on-premise and cloud apps, unstructured data and spreadsheets. Automated workflows make data integration faster and provide powerful data preparation tools that allow for transformative insights. Data integration tools that are intuitive and flexible allow users to connect and blend data from multiple sources such as data warehouses, IoT devices and cloud storage.

JMP is a data analysis tool compatible with both Mac and Windows that merges robust statistical capabilities with engaging interactive visualizations. The software simplifies the process of importing and analyzing data through its user-friendly drag-and-drop interface, interconnected graphs, an extensive library of advanced analytic features, a scripting language, and various sharing options, enabling users to explore their datasets more efficiently and effectively. Initially created in the 1980s to leverage the potential of graphical user interfaces for personal computing, JMP continues to evolve by incorporating innovative statistical techniques and specialized analysis methods from diverse industries with each new version released. Furthermore, John Sall, the founder of the organization, remains actively involved as the Chief Architect, ensuring the software stays at the forefront of analytical technology.

With more than three decades of expertise in data discovery and transformation, Altair Monarch stands out as an industry pioneer, providing the quickest and most user-friendly method for extracting data from a variety of sources. Users can easily create workflows without any coding knowledge, allowing for collaboration in transforming challenging data formats like PDFs, spreadsheets, text files, as well as data from big data sources and other structured formats into organized rows and columns. Regardless of whether the data is stored locally or in the cloud, Altair Monarch streamlines preparation tasks, leading to faster outcomes and delivering reliable data that supports informed business decision-making. This robust solution empowers organizations to harness their data effectively, ultimately driving growth and innovation. For more information about Altair Monarch or to access a free version of its enterprise software, please click the links provided below.

Amazon SageMaker Data Wrangler significantly shortens the data aggregation and preparation timeline for machine learning tasks from several weeks to just minutes. This tool streamlines data preparation and feature engineering, allowing you to execute every phase of the data preparation process—such as data selection, cleansing, exploration, visualization, and large-scale processing—through a unified visual interface. You can effortlessly select data from diverse sources using SQL, enabling rapid imports. Following this, the Data Quality and Insights report serves to automatically assess data integrity and identify issues like duplicate entries and target leakage. With over 300 pre-built data transformations available, SageMaker Data Wrangler allows for quick data modification without the need for coding. After finalizing your data preparation, you can scale the workflow to encompass your complete datasets, facilitating model training, tuning, and deployment in a seamless manner. This comprehensive approach not only enhances efficiency but also empowers users to focus on deriving insights from their data rather than getting bogged down in the preparation phase.

Kylo serves as an open-source platform designed for effective management of enterprise-level data lakes, facilitating self-service data ingestion and preparation while also incorporating robust metadata management, governance, security, and best practices derived from Think Big's extensive experience with over 150 big data implementation projects. It allows users to perform self-service data ingestion complemented by features for data cleansing, validation, and automatic profiling. Users can manipulate data effortlessly using visual SQL and an interactive transformation interface that is easy to navigate. The platform enables users to search and explore both data and metadata, examine data lineage, and access profiling statistics. Additionally, it provides tools to monitor the health of data feeds and services within the data lake, allowing users to track service level agreements (SLAs) and address performance issues effectively. Users can also create batch or streaming pipeline templates using Apache NiFi and register them with Kylo, thereby empowering self-service capabilities. Despite organizations investing substantial engineering resources to transfer data into Hadoop, they often face challenges in maintaining governance and ensuring data quality, but Kylo significantly eases the data ingestion process by allowing data owners to take control through its intuitive guided user interface. This innovative approach not only enhances operational efficiency but also fosters a culture of data ownership within organizations.

Keep a close eye on your data health and the performance of your pipelines. Achieve comprehensive oversight for pipelines utilizing cloud-native technologies such as Apache Airflow, Apache Spark, Snowflake, BigQuery, and Kubernetes. This observability platform is specifically designed for Data Engineers. As the challenges in data engineering continue to escalate due to increasing demands from business stakeholders, Databand offers a solution to help you keep pace. With the rise in the number of pipelines comes greater complexity. Data engineers are now handling more intricate infrastructures than they ever have before while also aiming for quicker release cycles. This environment makes it increasingly difficult to pinpoint the reasons behind process failures, delays, and the impact of modifications on data output quality. Consequently, data consumers often find themselves frustrated by inconsistent results, subpar model performance, and slow data delivery. A lack of clarity regarding the data being provided or the origins of failures fosters ongoing distrust. Furthermore, pipeline logs, errors, and data quality metrics are often gathered and stored in separate, isolated systems, complicating the troubleshooting process. To address these issues effectively, a unified observability approach is essential for enhancing trust and performance in data operations.

Effortlessly load your data into or extract it from Hadoop and data lakes, ensuring it is primed for generating reports, visualizations, or conducting advanced analytics—all within the data lakes environment. This streamlined approach allows you to manage, transform, and access data stored in Hadoop or data lakes through a user-friendly web interface, minimizing the need for extensive training. Designed specifically for big data management on Hadoop and data lakes, this solution is not simply a rehash of existing IT tools. It allows for the grouping of multiple directives to execute either concurrently or sequentially, enhancing workflow efficiency. Additionally, you can schedule and automate these directives via the public API provided. The platform also promotes collaboration and security by enabling the sharing of directives. Furthermore, these directives can be invoked from SAS Data Integration Studio, bridging the gap between technical and non-technical users. It comes equipped with built-in directives for various tasks, including casing, gender and pattern analysis, field extraction, match-merge, and cluster-survive operations. For improved performance, profiling processes are executed in parallel on the Hadoop cluster, allowing for the seamless handling of large datasets. This comprehensive solution transforms the way you interact with data, making it more accessible and manageable than ever.

Our innovative automated data operations platform empowers businesses to decrease or reassign the full-time staff required for their data management tasks. Traditionally, this has been a labor-intensive and costly endeavor, but our solution consolidates everything into a user-friendly package that streamlines the process and leads to substantial cost savings. Many organizations struggle to maintain data quality due to the overwhelming volume of problematic data they handle daily. By implementing our platform, companies transition into proactive entities regarding data integrity. With comprehensive visibility over incoming data and an integrated alert system, our platform guarantees adherence to your data quality standards. As a groundbreaking solution, we have transformed numerous expensive manual workflows into a cohesive platform. The BettrData.io platform is not only easy to implement but also requires just a few simple configurations to get started. This means that businesses can swiftly adapt to our system, ensuring they maximize efficiency from day one.

Effortlessly link your data to the engine by either uploading a file or establishing a connection to a database. Once connected, you can begin to explore your data through various visualizations, or you can prepare it for machine learning modeling using data wrangling techniques and reusable recipes. Maximize the potential of your data by constructing machine learning models with regression, classification, or clustering algorithms—all without requiring any coding skills. Discover valuable insights into your dataset through tools that highlight feature importance, explain predictions, and allow for scenario analysis. Additionally, you can make forecasts and easily integrate them into your current systems using our pre-configured connectors, enabling you to take immediate action based on your findings. This streamlined process empowers you to unlock the full value of your data and drive informed decision-making.

Data often necessitates thorough preprocessing to align with your specific requirements. Our AI streamlines the cumbersome process of data preparation and cleansing, effectively freeing up hours of your time. Research shows that data analysts dedicate approximately 80% of their time to this tedious and manual effort just to extract valuable insights. With the advent of AI, the landscape changes dramatically. For instance, it can convert text fields such as customer feedback into quantitative ratings ranging from 0 to 5. Moreover, it can detect trends in customer sentiments and generate new columns for sentiment analysis. By eliminating irrelevant columns, you can concentrate on the data that truly matters. This approach is further enhanced by integrating external data, providing you with a more holistic view of insights. Poor-quality data can result in flawed decision-making; thus, ensuring the quality and cleanliness of your data should be paramount in any data-driven strategy. You can be confident that we prioritize your privacy and do not use your data to improve our AI systems, meaning your information is kept strictly confidential. Additionally, we partner with the most reputable cloud service providers to safeguard your data effectively. This commitment to data security ensures that you can focus on deriving insights without worrying about data integrity.

SparkGrid, offered by Sparklabs, is a powerful data management solution that simplifies Snowflake communication by providing a tabularized interface that feels familiar to users of spreadsheet applications. This intuitive approach removes the need for advanced technical skills, enabling users of varying expertise to efficiently manage complex datasets within Snowflake. Key features include multi-field editing, real-time SQL statement previews, and robust built-in error handling and security protocols to protect data integrity and prevent unauthorized access. SparkGrid’s GUI enables seamless data operations such as adding, removing, and editing rows, columns, and tables without switching between visual tools and code. It supports Snowflake’s cloud data platform fully, promoting universal accessibility and empowering teams to collaborate better. The platform streamlines database interaction and boosts user productivity by simplifying traditionally complex tasks. SparkGrid is also available on AWS Marketplace, making deployment easier for cloud users. By democratizing access to Snowflake data management, SparkGrid drives informed decision-making and innovation.

Power Query provides a user-friendly solution for connecting, extracting, transforming, and loading data from a variety of sources. Acting as a robust engine for data preparation and transformation, Power Query features a graphical interface that simplifies the data retrieval process and includes a Power Query Editor for implementing necessary changes. The versatility of the engine allows it to be integrated across numerous products and services, meaning the storage location of the data is determined by the specific application of Power Query. This tool enables users to efficiently carry out the extract, transform, and load (ETL) processes for their data needs. With Microsoft’s Data Connectivity and Data Preparation technology, users can easily access and manipulate data from hundreds of sources in a straightforward, no-code environment. Power Query is equipped with support for a multitude of data sources through built-in connectors, generic interfaces like REST APIs, ODBC, OLE, DB, and OData, and even offers a Power Query SDK for creating custom connectors tailored to individual requirements. This flexibility makes Power Query an indispensable asset for data professionals seeking to streamline their workflows.

Coheris Spad, developed by ChapsVision, serves as a self-service data analysis platform tailored for Data Scientists across diverse sectors and industries. This tool is widely recognized and incorporated into numerous prestigious French and international educational institutions, solidifying its esteemed status among Data Scientists. Coheris Spad offers an extensive methodological framework that encompasses a wide array of data analysis techniques. Users benefit from a friendly and intuitive interface that equips them with the necessary capabilities to explore, prepare, and analyze their data effectively. The platform supports connections to multiple data sources for efficient data preparation. Additionally, it boasts a comprehensive library of data processing functions, including filtering, stacking, aggregation, transposition, joining, handling of missing values, identification of unusual distributions, statistical or supervised recoding, and formatting options, empowering users to perform thorough and insightful analyses. Furthermore, the flexibility and versatility of Coheris Spad make it an invaluable asset for both novice and experienced data practitioners.

TROCCO is an all-in-one modern data platform designed to help users seamlessly integrate, transform, orchestrate, and manage data through a unified interface. It boasts an extensive array of connectors that encompass advertising platforms such as Google Ads and Facebook Ads, cloud services like AWS Cost Explorer and Google Analytics 4, as well as various databases including MySQL and PostgreSQL, and data warehouses such as Amazon Redshift and Google BigQuery. One of its standout features is Managed ETL, which simplifies the data import process by allowing bulk ingestion of data sources and offers centralized management for ETL configurations, thereby removing the necessity for individual setup. Furthermore, TROCCO includes a data catalog that automatically collects metadata from data analysis infrastructure, creating a detailed catalog that enhances data accessibility and usage. Users have the ability to design workflows that enable them to organize a sequence of tasks, establishing an efficient order and combination to optimize data processing. This capability allows for increased productivity and ensures that users can better capitalize on their data resources.

Xtract Data Automation Suite (XDAS) is a comprehensive platform designed to streamline process automation for data-intensive workflows. It offers a vast library of over 300 pre-built micro solutions and AI agents, enabling businesses to design and orchestrate AI-driven workflows with no code environment, thereby enhancing operational efficiency and accelerating digital transformation. By leveraging these tools, XDAS helps businesses ensure compliance, reduce time to market, enhance data accuracy, and forecast market trends across various industries.

Numerous individuals rely on Excel, VBA/Python scripts, or SQL queries for preparing data, often due to a lack of awareness of superior options available. EasyMorph stands out as a dedicated tool that offers over 150 built-in actions designed for quick and visual data transformation and automation, all without the need for coding skills. By utilizing EasyMorph, you can move beyond complex scripts and unwieldy spreadsheets, significantly enhancing your productivity. This application allows you to seamlessly retrieve data from a variety of sources such as databases, spreadsheets, emails and their attachments, text files, remote folders, corporate applications like SharePoint, and web APIs, all without needing programming expertise. You can employ visual tools and queries to filter and extract precisely the information you require, eliminating the need to consult IT for assistance. Moreover, it enables you to automate routine tasks associated with files, spreadsheets, websites, and emails with no coding required, transforming tedious and repetitive actions into a simple button click. With EasyMorph, not only is the data preparation process simplified, but users can also focus on more strategic tasks instead of getting bogged down in the minutiae of data handling.

Experience an enterprise-level architecture and a cloud data platform powered by Snowflake® that enables secure storage and utilization of your data in the cloud. With integrated ETL and an intuitive drag-and-drop visual workflow designer, you can easily connect, clean, and transform data from over 250 sources. Harness cutting-edge Search and AI technology to quickly generate insights and actionable analytics from your data within seconds. Utilize our advanced AI/ML environments to create, refine, and deploy your predictive analytics and forecasting models. Take your data capabilities further with our comprehensive AI/ML frameworks, allowing you to design, train, and implement AI models through the PurpleCube Data Science module. Additionally, construct engaging BI visualizations with PurpleCube Analytics, explore your data using natural language searches, and benefit from AI-driven insights and intelligent recommendations that reveal answers to questions you may not have considered. This holistic approach ensures that you are equipped to make data-driven decisions with confidence and clarity.

Create, execute, and oversee AI models while enhancing decision-making at scale across any cloud infrastructure. IBM Watson Studio enables you to implement AI seamlessly anywhere as part of the IBM Cloud Pak® for Data, which is the comprehensive data and AI platform from IBM. Collaborate across teams, streamline the management of the AI lifecycle, and hasten the realization of value with a versatile multicloud framework. You can automate the AI lifecycles using ModelOps pipelines and expedite data science development through AutoAI. Whether preparing or constructing models, you have the option to do so visually or programmatically. Deploying and operating models is made simple with one-click integration. Additionally, promote responsible AI governance by ensuring your models are fair and explainable to strengthen business strategies. Leverage open-source frameworks such as PyTorch, TensorFlow, and scikit-learn to enhance your projects. Consolidate development tools, including leading IDEs, Jupyter notebooks, JupyterLab, and command-line interfaces, along with programming languages like Python, R, and Scala. Through the automation of AI lifecycle management, IBM Watson Studio empowers you to build and scale AI solutions with an emphasis on trust and transparency, ultimately leading to improved organizational performance and innovation.

Upsolver makes it easy to create a governed data lake, manage, integrate, and prepare streaming data for analysis. Only use auto-generated schema on-read SQL to create pipelines. A visual IDE that makes it easy to build pipelines. Add Upserts to data lake tables. Mix streaming and large-scale batch data. Automated schema evolution and reprocessing of previous state. Automated orchestration of pipelines (no Dags). Fully-managed execution at scale Strong consistency guarantee over object storage Nearly zero maintenance overhead for analytics-ready information. Integral hygiene for data lake tables, including columnar formats, partitioning and compaction, as well as vacuuming. Low cost, 100,000 events per second (billions every day) Continuous lock-free compaction to eliminate the "small file" problem. Parquet-based tables are ideal for quick queries.

Invenis serves as a robust platform for data analysis and mining, enabling users to easily clean, aggregate, and analyze their data while scaling efforts to enhance decision-making processes. It offers capabilities such as data harmonization, preparation, cleansing, enrichment, and aggregation, alongside powerful predictive analytics, segmentation, and recommendation features. By connecting seamlessly to various data sources like MySQL, Oracle, Postgres SQL, and HDFS (Hadoop), Invenis facilitates comprehensive analysis of diverse file formats, including CSV and JSON. Users can generate predictions across all datasets without requiring coding skills or a specialized team of experts, as the platform intelligently selects the most suitable algorithms based on the specific data and use cases presented. Additionally, Invenis automates repetitive tasks and recurring analyses, allowing users to save valuable time and fully leverage the potential of their data. Collaboration is also enhanced, as teams can work together, not only among analysts but across various departments, streamlining decision-making processes and ensuring that information flows efficiently throughout the organization. This collaborative approach ultimately empowers businesses to make better-informed decisions based on timely and accurate data insights.

Verodat, a SaaS-platform, gathers, prepares and enriches your business data, then connects it to AI Analytics tools. For results you can trust. Verodat automates data cleansing & consolidates data into a clean, trustworthy data layer to feed downstream reporting. Manages data requests for suppliers. Monitors data workflows to identify bottlenecks and resolve issues. The audit trail is generated to prove quality assurance for each data row. Validation & governance can be customized to your organization. Data preparation time is reduced by 60% allowing analysts to focus more on insights. The central KPI Dashboard provides key metrics about your data pipeline. This allows you to identify bottlenecks and resolve issues, as well as improve performance. The flexible rules engine allows you to create validation and testing that suits your organization's requirements. It's easy to integrate your existing tools with the out-of-the box connections to Snowflake and Azure.

DataPreparator is a complimentary software application aimed at facilitating various aspects of data preparation, also known as data preprocessing, within the realms of data analysis and mining. This tool provides numerous functionalities to help you explore and ready your data before engaging in analysis or mining activities. It encompasses a range of features including data cleaning, discretization, numerical adjustments, scaling, attribute selection, handling missing values, addressing outliers, conducting statistical analyses, visualizations, balancing, sampling, and selecting specific rows, among other essential tasks. The software allows users to access data from various sources such as text files, relational databases, and Excel spreadsheets. It is capable of managing substantial data volumes effectively, as datasets are not retained in computer memory, except for Excel files and the result sets from certain databases that lack data streaming support. As a standalone tool, it operates independently of other applications, boasting a user-friendly graphical interface. Additionally, it enables operator chaining to form sequences of preprocessing transformations and allows for the creation of a model tree specifically for test or execution data, thereby enhancing the overall data preparation process. Ultimately, DataPreparator serves as a versatile and efficient resource for those engaged in data-related tasks.

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.

An agile platform for end-to-end DataOps that not only enhances but also protects your data assets is available through Arena, the leading augmented data management solution. With our dynamic data catalog, users can enrich and access data independently, facilitating efficient management of intricate data landscapes. Tailored workflows enhance the precision and dependability of every dataset, while machine learning identifies and aligns master data assets to facilitate superior decision-making. Comprehensive lineage tracking, accompanied by intricate visualizations and advanced security measures like masking and tokenization, ensures utmost protection. Our platform simplifies data management by cataloging data from any location, with flexible connections that allow analytics to integrate seamlessly with your chosen tools. Additionally, our software effectively addresses the challenges of data sprawl, driving success in business and analytics while offering essential controls and adaptability in today’s diverse, multi-cloud data environments. As organizations increasingly rely on data, Arena stands out as a vital partner in navigating this complexity.

MassFeeds serves as a specialized tool for data preparation that automates and expedites the organization of data originating from diverse sources and formats. This innovative solution is crafted to enhance and streamline the data preparation workflow by generating automated data pipelines specifically tailored for marketing mix models. As the volume of data generation and collection continues to surge, organizations can no longer rely on labor-intensive manual processes for data preparation to keep pace. MassFeeds empowers clients to efficiently manage data from various origins and formats through a smooth, automated, and easily adjustable approach. By utilizing MassFeeds’ suite of processing pipelines, data is transformed into a standardized format, ensuring effortless integration into modeling systems. This tool helps eliminate the risks associated with manual data preparation, which can often lead to human errors. Moreover, it broadens access to data processing for a larger range of users and boasts the potential to reduce processing times by over 40% by automating repetitive tasks, ultimately leading to more efficient operations across the board. With MassFeeds, organizations can experience a significant boost in their data management capabilities.

Successful enterprises often share key characteristics: enhancing operational efficiencies, managing risks, increasing revenue, and driving rapid innovation. Data360 Analyze provides the quickest means to consolidate and structure extensive datasets, revealing crucial insights across various business divisions. Users can effortlessly access, prepare, and analyze high-quality data via its user-friendly web-based interface. Gaining a comprehensive grasp of your organization's data environment can illuminate various data sources, including those that are incomplete, erroneous, or inconsistent. This platform enables the swift identification, validation, transformation, and integration of data from all corners of your organization, ensuring the delivery of precise, pertinent, and reliable information for thorough analysis. Moreover, features like visual data examination and tracking empower users to monitor and retrieve data at any stage of the analytical workflow, fostering collaboration among stakeholders and enhancing confidence in the data and findings produced. In doing so, organizations can make more informed decisions based on trustworthy insights derived from robust data analysis.

Zoho DataPrep is an advanced self-service data preparation software that helps organizations prepare data by allowing import from a variety of sources, automatically identifying errors, discovering data patterns, transforming and enriching data and scheduling export all without the need for coding.

A powerful labeling platform for all stages and types of ML development. We leverage a suite of industry-leading computer vision algorithms to automatically detect and classify the content of your images and videos. Creating detailed segmentation information is a time-consuming process. Machine assistance speeds up task completion by as much as 70%, saving you both time and money. We leverage ML to propose labels that accelerate human labeling. This includes computer vision models to automatically detect, localize, and classify entities in your images and videos before handing off the task to our workforce. Automatic labelling reduces workforce costs and allows annotators to spend their time on the more complicated steps of the annotation process. Our video annotation tool is built to handle 4K resolution and long-running videos natively and provides innovative features like interpolation, object proposal, and entity resolution.

If you're looking to establish a governed delta lake, create a data warehouse, or transition from a conventional database to a contemporary cloud data solution, Lyftrondata has you covered. You can effortlessly create and oversee all your data workloads within a single platform, automating the construction of your pipeline and warehouse. Instantly analyze your data using ANSI SQL and business intelligence or machine learning tools, and easily share your findings without the need for custom coding. This functionality enhances the efficiency of your data teams and accelerates the realization of value. You can define, categorize, and locate all data sets in one centralized location, enabling seamless sharing with peers without the complexity of coding, thus fostering insightful data-driven decisions. This capability is particularly advantageous for organizations wishing to store their data once, share it with various experts, and leverage it repeatedly for both current and future needs. In addition, you can define datasets, execute SQL transformations, or migrate your existing SQL data processing workflows to any cloud data warehouse of your choice, ensuring flexibility and scalability in your data management strategy.

Foundry is a transformative data platform built to help solve the modern enterprise’s most critical problems by creating a central operating system for an organization’s data, while securely integrating siloed data sources into a common analytics and operations picture. Palantir works with commercial companies and government organizations alike to close the operational loop, feeding real-time data into your data science models and updating source systems. With a breadth of industry-leading capabilities, Palantir can help enterprises traverse and operationalize data to enable and scale decision-making, alongside best-in-class security, data protection, and governance. Foundry was named by Forrester as a leader in the The Forrester Wave™: AI/ML Platforms, Q3 2022. Scoring the highest marks possible in product vision, performance, market approach, and applications criteria. As a Dresner-Award winning platform, Foundry is the overall leader in the BI and Analytics market and rated a perfect 5/5 by its customer base.

The BiG EVAL platform offers robust software tools essential for ensuring and enhancing data quality throughout the entire information lifecycle. Built on a comprehensive and versatile code base, BiG EVAL's data quality management and testing tools are designed for peak performance and adaptability. Each feature has been developed through practical insights gained from collaborating with our clients. Maintaining high data quality across the full lifecycle is vital for effective data governance and is key to maximizing business value derived from your data. This is where the BiG EVAL DQM automation solution plays a critical role, assisting you with all aspects of data quality management. Continuous quality assessments validate your organization’s data, furnish quality metrics, and aid in addressing any quality challenges. Additionally, BiG EVAL DTA empowers you to automate testing processes within your data-centric projects, streamlining operations and enhancing efficiency. By integrating these tools, organizations can achieve a more reliable data environment that fosters informed decision-making.

Astronomer is the driving force behind Apache Airflow, the de facto standard for expressing data flows as code. Airflow is downloaded more than 4 million times each month and is used by hundreds of thousands of teams around the world. For data teams looking to increase the availability of trusted data, Astronomer provides Astro, the modern data orchestration platform, powered by Airflow. Astro enables data engineers, data scientists, and data analysts to build, run, and observe pipelines-as-code. Founded in 2018, Astronomer is a global remote-first company with hubs in Cincinnati, New York, San Francisco, and San Jose. Customers in more than 35 countries trust Astronomer as their partner for data orchestration.

Streamline data accessibility across various platforms and applications, enabling exploration, preparation, blending, analysis, and the provision of bot-generated insights whenever required. Design workflows in mere minutes to automate every phase of analytics, from initial data acquisition to the final presentation of insights, effectively eliminating shadow analytics. Foster collaboration among all stakeholders on a unified platform while ensuring auditability and governance of workflows. This comprehensive platform caters to supply chain, HR, sales, and marketing analytics, seamlessly integrating tools like Fivetran, Snowflake, DBT, Workday, Pendo, Marketo, and PowerBI. With a no-code approach, Savant empowers users to connect, transform, and analyze data using familiar functions found in Excel and SQL, all while making every step automatable. By minimizing the burden of manually handling data, you can redirect your focus toward insightful analysis and strategic decision-making, enhancing overall productivity.

Data serves as the essential driving force behind any effective sales and marketing strategy. It is important to note that data loses its value at a rate of 2% each month. Additionally, the effectiveness of data gathered through email marketing diminishes by approximately 22.5% annually. Without precise data, a business’s marketing approach can significantly falter. Consequently, maintaining an accurate and up-to-date database is crucial. Binary Demands offers a global contact database designed to transform your marketing campaigns and strategies. Over time, your collected data naturally deteriorates, which can hinder your efforts. To combat this issue, Binary Demand delivers tailored solutions that address data degradation, ensuring that your information remains useful. These customized data services encompass standardization, de-duplication, cleansing, and verification, allowing for the creation of targeted customer lists based on factors like location, company size, job titles, and industry. With our commitment to high accuracy and a cost-effective model, we position ourselves as the top return on investment-generating list partner in the industry, enabling clients to make informed decisions and drive sales effectively.

Quickly prepare data to provide trusted insights across the organization. Business analysts and data scientists spend too much time cleaning out data rather than analyzing it. Talend Data Preparation is a self-service, browser-based tool that allows you to quickly identify errors and create rules that can be reused and shared across large data sets. With our intuitive user interface and self-service data preparation/curation functionality, anyone can perform data profiling, cleansing, enriching and enrichment in real time. Users can share prepared datasets and curated data, and embed data preparations in batch, bulk, or live data integration scenarios. Talend allows you to transform ad-hoc analysis and data enrichment jobs into fully managed, reusable process. You can use any data source, including Teradata and AWS, Salesforce and Marketo, to operationalize data preparation. Always using the most recent datasets. Talend Data Preparation gives you control over data governance.

Datameer is your go-to data tool for exploring, preparing, visualizing, and cataloging Snowflake insights. From exploring raw datasets to driving business decisions – an all-in-one tool.

Anzo is an innovative platform for data discovery and integration that empowers users to locate, connect, and blend various enterprise data into datasets that are ready for analysis. With its distinctive application of semantics and graph data models, Anzo enables individuals across the organization—from expert data scientists to inexperienced business users—to actively participate in the data discovery and integration journey, crafting their own analytics-ready datasets in the process. The graph data models offered by Anzo create a visual representation of enterprise data, simplifying the navigation and understanding of complex and siloed information. By incorporating semantics, Anzo enriches the data with business context, allowing users to unify data according to shared definitions and create blended datasets that are tailored for immediate business needs. This democratization of data access not only fosters collaboration but also accelerates decision-making across various levels of the organization.

Self-service analytics tools were designed to empower end-users by enhancing their agility and fostering a data-driven culture. Unfortunately, this boost in agility often resulted in fragmented and isolated workflows due to a lack of data governance, leading to chaotic data management practices. Knowledge Hub offers a solution that effectively tackles these challenges, benefiting business users while simultaneously streamlining and fortifying IT governance. Featuring an easy-to-use browser-based interface, it automates the tasks involved in data transformation, making it the only collaborative data preparation tool available in today's market. This enables business teams to collaborate effortlessly with data engineers and scientists, providing a tailored experience for creating, validating, and sharing datasets and analytical models that are both governed and reliable. With no coding necessary, a wider audience can contribute to collaborative efforts, ultimately leading to better-informed decision-making. Governance, data lineage, and collaboration are seamlessly managed within a cloud-compatible solution specifically designed to foster innovation. Additionally, the platform's extensibility and low- to no-code capabilities empower individuals from various departments to efficiently transform data, encouraging a culture of shared insights and collaboration throughout the organization.

The fundamental technology that powers contemporary solutions for data integration and management is designed to swiftly link various structured and unstructured data sources. It allows for the comprehensive cataloging of your entire data environment, ensuring that data remains within its original sources and is retrieved as needed, eliminating the requirement for duplicate copies. Users can construct data models tailored to their needs, even when drawing from multiple data sources, while also concealing the intricacies of back-end systems from end users. The virtual model can be securely accessed and utilized through standard SQL alongside other formats such as REST, SOAP, and OData, promoting easy access to diverse data types. It features complete data integration and modeling capabilities, along with an Active Data Catalog that enables self-service for data and metadata exploration and preparation. Furthermore, it incorporates robust data security and governance measures, ensures rapid and intelligent execution of data queries, and provides real-time data delivery in various formats. The system also supports the establishment of data marketplaces and effectively decouples business applications from data systems, paving the way for more informed, data-driven decision-making strategies. This innovative approach enhances the overall agility and responsiveness of organizations in managing their data assets.

Enhance your analytics, data migration, and master data management (MDM) projects with the SAP Agile Data Preparation tool. This application allows you to efficiently convert your data into actionable insights, streamlining your access to and understanding of data shapes, thus increasing your productivity and agility beyond your expectations. The Cloud Service's Usage Metric is measured by the number of Users, defined as individuals who prepare, manage, and oversee data sets or perform data stewardship tasks within the service. Each subscription requires customers to purchase an annual foundation subscription, which comes in increments of 64 GB of memory per year, with a maximum capacity of 512 GB annually. This structured approach ensures that organizations can scale their data needs effectively while maintaining high performance and efficiency.

Transforming a week's labor of manual data preparation into mere minutes, our innovative Data Preparer software streamlines the path to insights through intelligent data handling. This fresh approach to data preparation allows users to specify their requirements, letting the software automatically determine the best way to fulfill them. With Data Preparer, labor-intensive programming is no longer necessary, as it efficiently manages data preparation tasks without the need for intricate coding. Users simply outline their needs, supplying data sources, a desired structure, quality benchmarks, and sample data. The clarity provided by the target structure and quality priorities ensures precise requirements, while the example data aids Data Preparer in efficiently cleaning and integrating the datasets. Once the parameters are set, Data Preparer takes over, analyzing relationships between the various data sources and the intended target, effectively populating the target with the necessary information. Moreover, it assesses multiple methods for combining the sources and adapts the data format accordingly, making the entire process seamless and user-friendly. In this way, Data Preparer not only simplifies the data preparation process but also enhances the overall quality of the analysis.