Alternatives to Datakin

dbt Labs is redefining how data teams work with SQL. Instead of waiting on complex ETL processes, dbt lets data analysts and data engineers build production-ready transformations directly in the warehouse, using code, version control, and CI/CD. This community-driven approach puts power back in the hands of practitioners while maintaining governance and scalability for enterprise use. With a rapidly growing open-source community and an enterprise-grade cloud platform, dbt is at the heart of the modern data stack. It’s the go-to solution for teams who want faster analytics, higher quality data, and the confidence that comes from transparent, testable transformations.

Accelerate your data journey with AnalyticsCreator—a metadata-driven data warehouse automation solution purpose-built for the Microsoft data ecosystem. AnalyticsCreator simplifies the design, development, and deployment of modern data architectures, including dimensional models, data marts, data vaults, or blended modeling approaches tailored to your business needs. Seamlessly integrate with Microsoft SQL Server, Azure Synapse Analytics, Microsoft Fabric (including OneLake and SQL Endpoint Lakehouse environments), and Power BI. AnalyticsCreator automates ELT pipeline creation, data modeling, historization, and semantic layer generation—helping reduce tool sprawl and minimizing manual SQL coding. Designed to support CI/CD pipelines, AnalyticsCreator connects easily with Azure DevOps and GitHub for version-controlled deployments across development, test, and production environments. This ensures faster, error-free releases while maintaining governance and control across your entire data engineering workflow. Key features include automated documentation, end-to-end data lineage tracking, and adaptive schema evolution—enabling teams to manage change, reduce risk, and maintain auditability at scale. AnalyticsCreator empowers agile data engineering by enabling rapid prototyping and production-grade deployments for Microsoft-centric data initiatives. By eliminating repetitive manual tasks and deployment risks, AnalyticsCreator allows your team to focus on delivering actionable business insights—accelerating time-to-value for your data products and analytics initiatives.

The Alation Agentic Data Intelligence Platform is designed to transform how enterprises manage, govern, and use data for AI and analytics. It combines search, cataloging, governance, lineage, and analytics into one unified solution, turning metadata into actionable insights. AI-powered agents automate critical tasks like documentation, data quality monitoring, and product creation, freeing teams from repetitive manual work. Its Active Metadata Graph and workflow automation capabilities ensure that data remains accurate, consistent, and trustworthy across systems. With 120+ pre-built connectors, including integrations with AWS, Snowflake, Salesforce, and Databricks, Alation integrates seamlessly into enterprise ecosystems. The platform enables organizations to govern AI responsibly, ensuring compliance, transparency, and ethical use of data. Enterprises benefit from improved self-service analytics, faster data-driven decisions, and a stronger data culture. With industry leaders like Salesforce and 40% of the Fortune 100 relying on it, Alation is proven to help businesses unlock the value of their data.

Manta is a unified data lineage platform that serves as the central hub of all enterprise data flows. Manta can construct lineage from report definitions, custom SQL code, and ETL workflows. Lineage is analyzed based on actual code, and both direct and indirect flows can be visualized on the map. Data paths between files, report fields, database tables, and individual columns are displayed to users in an intuitive user interface, enabling teams to understand data flows in context.

Detect and address code and optimization challenges in real-time, mitigate data incidents before deployment, and oversee data-affecting code modifications comprehensively—from the operational database to the user interface dashboard. With automated, column-level data lineage tracing the journey from the operational database to the reporting layer, every dependency is meticulously examined. Foundational automates the enforcement of data contracts by scrutinizing each repository in both upstream and downstream directions, directly from the source code. Leverage Foundational to proactively uncover code and data-related issues, prevent potential problems, and establish necessary controls and guardrails. Moreover, implementing Foundational can be achieved in mere minutes without necessitating any alterations to the existing codebase, making it an efficient solution for organizations. This streamlined setup promotes quicker response times to data governance challenges.

Aggua serves as an augmented AI platform for data fabric that empowers both data and business teams to access their information, fostering trust while providing actionable data insights, ultimately leading to more comprehensive, data-driven decision-making. Rather than being left in the dark about the intricacies of your organization's data stack, you can quickly gain clarity with just a few clicks. This platform offers insights into data costs, lineage, and documentation without disrupting your data engineer’s busy schedule. Instead of investing excessive time on identifying how a change in data type might impact your data pipelines, tables, and overall infrastructure, automated lineage allows data architects and engineers to focus on implementing changes rather than sifting through logs and DAGs. As a result, teams can work more efficiently and effectively, leading to faster project completions and improved operational outcomes.

Effortlessly monitor thousands of tables through machine learning-driven anomaly detection alongside a suite of over 50 tailored metrics. Ensure comprehensive oversight of both data and metadata while meticulously mapping all asset dependencies from ingestion to business intelligence. This solution enhances productivity and fosters collaboration between data engineers and consumers. Sifflet integrates smoothly with your existing data sources and tools, functioning on platforms like AWS, Google Cloud Platform, and Microsoft Azure. Maintain vigilance over your data's health and promptly notify your team when quality standards are not satisfied. With just a few clicks, you can establish essential coverage for all your tables. Additionally, you can customize the frequency of checks, their importance, and specific notifications simultaneously. Utilize machine learning-driven protocols to identify any data anomalies with no initial setup required. Every rule is supported by a unique model that adapts based on historical data and user input. You can also enhance automated processes by utilizing a library of over 50 templates applicable to any asset, thereby streamlining your monitoring efforts even further. This approach not only simplifies data management but also empowers teams to respond proactively to potential issues.

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.

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.

Decube is a comprehensive data management platform designed to help organizations manage their data observability, data catalog, and data governance needs. Our platform is designed to provide accurate, reliable, and timely data, enabling organizations to make better-informed decisions. Our data observability tools provide end-to-end visibility into data, making it easier for organizations to track data origin and flow across different systems and departments. With our real-time monitoring capabilities, organizations can detect data incidents quickly and reduce their impact on business operations. The data catalog component of our platform provides a centralized repository for all data assets, making it easier for organizations to manage and govern data usage and access. With our data classification tools, organizations can identify and manage sensitive data more effectively, ensuring compliance with data privacy regulations and policies. The data governance component of our platform provides robust access controls, enabling organizations to manage data access and usage effectively. Our tools also allow organizations to generate audit reports, track user activity, and demonstrate compliance with regulatory requirements.

Experience the implications of data-related problems without the need to execute SQL queries. Our approach involves a thorough analysis of your logs and metadata to uncover issues such as freshness and volume discrepancies, changes in table schemas, and errors within pipelines, along with their potential impacts on your business operations. Masthead continuously monitors all tables, processes, scripts, and dashboards in your data warehouse and integrated BI tools, providing immediate alerts to data teams whenever failures arise. It reveals the sources and consequences of data anomalies and pipeline errors affecting consumers of the data. By mapping data problems onto lineage, Masthead enables you to resolve issues quickly, often within minutes rather than spending hours troubleshooting. The ability to gain a complete overview of all operations within GCP without granting access to sensitive data has proven transformative for us, ultimately leading to significant savings in both time and resources. Additionally, you can achieve insights into the expenses associated with each pipeline operating in your cloud environment, no matter the ETL method employed. Masthead is equipped with AI-driven recommendations designed to enhance the performance of your models and queries. Connecting Masthead to all components within your data warehouse takes just 15 minutes, making it a swift and efficient solution for any organization. This streamlined integration not only accelerates diagnostics but also empowers data teams to focus on more strategic initiatives.

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.

Tokern offers an open-source suite designed for data governance, specifically tailored for databases and data lakes. This user-friendly toolkit facilitates the collection, organization, and analysis of metadata from data lakes, allowing users to execute quick tasks via a command-line application or run it as a service for ongoing metadata collection. Users can delve into aspects like data lineage, access controls, and personally identifiable information (PII) datasets, utilizing reporting dashboards or Jupyter notebooks for programmatic analysis. As a comprehensive solution, Tokern aims to enhance your data's return on investment, ensure compliance with regulations such as HIPAA, CCPA, and GDPR, and safeguard sensitive information against insider threats seamlessly. It provides centralized management for metadata related to users, datasets, and jobs, which supports various other data governance functionalities. With the capability to track Column Level Data Lineage for platforms like Snowflake, AWS Redshift, and BigQuery, users can construct lineage from query histories or ETL scripts. Additionally, lineage exploration can be achieved through interactive graphs or programmatically via APIs or SDKs, offering a versatile approach to understanding data flow. Overall, Tokern empowers organizations to maintain robust data governance while navigating complex regulatory landscapes.

SQLFlow offers a comprehensive visual overview of data flow through various systems. It automates the analysis of SQL data lineage across a multitude of platforms, including databases, ETL processes, business intelligence tools, and environments like cloud and Hadoop, by effectively parsing SQL scripts and stored procedures. The tool graphically illustrates all data movements, supporting over 20 leading databases and continuously expanding its capabilities. It allows for automation in lineage construction regardless of the SQL's location, whether in databases, file systems, or repositories such as GitHub and Bitbucket. The user-friendly interface ensures that data flows are presented in a clear and easily understandable manner. By providing complete visibility into your business intelligence environment, it aids in pinpointing the root causes of reporting errors, fostering invaluable confidence in business processes. Additionally, it streamlines regulatory compliance efforts, while the visualization of data lineage enhances transparency and auditability. Users can conduct impact analysis at a detailed level, enabling a thorough examination of lineage down to tables, columns, and queries. With SQLFlow, you can seamlessly integrate powerful data lineage analysis capabilities into your product, thereby elevating your data management strategy. This tool not only simplifies complex tasks but also empowers teams to make informed decisions based on reliable insights.

Mozart Data is the all-in-one modern data platform for consolidating, organizing, and analyzing your data. Set up a modern data stack in an hour, without any engineering. Start getting more out of your data and making data-driven decisions today.

The Databricks Data Intelligence Platform empowers every member of your organization to leverage data and artificial intelligence effectively. Constructed on a lakehouse architecture, it establishes a cohesive and transparent foundation for all aspects of data management and governance, enhanced by a Data Intelligence Engine that recognizes the distinct characteristics of your data. Companies that excel across various sectors will be those that harness the power of data and AI. Covering everything from ETL processes to data warehousing and generative AI, Databricks facilitates the streamlining and acceleration of your data and AI objectives. By merging generative AI with the integrative advantages of a lakehouse, Databricks fuels a Data Intelligence Engine that comprehends the specific semantics of your data. This functionality enables the platform to optimize performance automatically and manage infrastructure in a manner tailored to your organization's needs. Additionally, the Data Intelligence Engine is designed to grasp the unique language of your enterprise, making the search and exploration of new data as straightforward as posing a question to a colleague, thus fostering collaboration and efficiency. Ultimately, this innovative approach transforms the way organizations interact with their data, driving better decision-making and insights.

Examine the usage of your data assets, focusing on aspects like popularity, utilization, and schema coverage. Gain vital insights into your data assets, including their quality and usage metrics. You can easily locate and filter the necessary data by leveraging metadata tags and descriptions. Additionally, these insights will help you drive data governance and establish clear ownership within your organization. By implementing a streamlined lineage from data lakes to warehouses, you can enhance collaboration and accountability. An automatically generated field-level lineage map provides a comprehensive view of your entire data ecosystem. Moreover, anomaly detection systems adapt by learning from your data trends and seasonal variations, ensuring automatic backfilling with historical data. Thresholds driven by machine learning are specifically tailored for each data segment, relying on actual data rather than just metadata to ensure accuracy and relevance. This holistic approach empowers organizations to better manage their data landscape effectively.

In 30 minutes, you can monitor your entire warehouse. Automated warehouse-to-BI lineage can identify downstream impacts. Trust can be lost in seconds and regained in months. With modern data-era observability, you can have peace of mind. It can be difficult to get the coverage you need with code-based tests. They take hours to create and maintain. Metaplane allows you to add hundreds of tests in minutes. Foundational tests (e.g. We support foundational tests (e.g. row counts, freshness and schema drift), more complicated tests (distribution shifts, nullness shiftings, enum modifications), custom SQL, as well as everything in between. Manual thresholds can take a while to set and quickly become outdated as your data changes. Our anomaly detection algorithms use historical metadata to detect outliers. To minimize alert fatigue, monitor what is important, while also taking into account seasonality, trends and feedback from your team. You can also override manual thresholds.

Dataplex Universal Catalog provides enterprise-wide visibility and governance for structured, semi-structured, and unstructured data. Its AI-powered semantic search allows users to query data in natural language, eliminating the need for complex search syntax. The platform enriches metadata with business context through glossaries, ownership attributes, and key usage details, supporting informed decision-making. It offers automated metadata ingestion, classification, and enrichment, reducing manual data management tasks. With built-in lineage tracking, organizations can trace data origins, transformations, and dependencies across multiple sources. BigQuery integration brings these governance capabilities directly into the analytics workflow, enhancing productivity. By connecting with BigLake, Dataplex extends governance to open lakehouses with Apache Iceberg and other engines. The result is a secure, scalable foundation for managing data-to-AI lifecycles across cloud-native and open-source ecosystems.

In just 15 minutes, you can set up your automated data catalogue and receive column-level lines, Entity Relationship diagrams, and auto-populated documentation in 24 hours. You can easily tag, find, and add documentation to data so everyone can find the right one for them. Select Star automatically detects your column-level data lineage and displays it. Now you can trust the data by knowing where it came. Select Star automatically displays how your company uses data. This allows you to identify relevant data fields without having to ask anyone else. Select Star ensures that your data is protected with AICPA SOC2 Security, Confidentiality and Availability standards.

To have complete control over your data, harness the power of data discovery, data lineage and a data catalogue. It can quickly navigate through complex data landscapes. Access the most comprehensive automated data lineage and discovery system. This gives you unprecedented visibility and trust in the most complex data environments. Octopai extracts metadata from all data environments. Octopai can instantly analyze metadata in a fast, secure, and easy process. Octopai gives you access to data lineage, data discovery, and a data catalogue, all from one central platform. In seconds, trace any data from end to end through your entire data landscape. Find the data you need automatically from any place in your data landscape. A self-creating, self updating data catalog will help you create consistency across your company.

Speeding up the process of gaining insights and removing obstacles for data analysts is crucial. With the help of intelligent automation in the data stack, you can extract insights from your data much faster—up to ten times quicker—thanks to AI innovations. Originally developed at Stanford's AI lab, this cutting-edge intelligence for today’s data stack is now accessible for your organization. You can leverage natural language to derive value from your disorganized, intricate, and isolated data within just minutes. Simply instruct your data on what you want to achieve, and it will promptly produce the necessary code for execution. This automation is highly customizable, tailored to the unique complexities of your organization rather than relying on generic templates. It empowers individuals to securely automate data-heavy workflows on the modern data stack, alleviating the burden on data engineers from a never-ending queue of requests. Experience the ability to reach insights in mere minutes instead of waiting months, with solutions that are specifically crafted and optimized for your organization’s requirements. Moreover, it integrates seamlessly with various upstream and downstream tools such as Snowflake, Databricks, Redshift, and BigQuery, all while being built on dbt, ensuring a comprehensive approach to data management. This innovative solution not only enhances efficiency but also promotes a culture of data-driven decision-making across all levels of your enterprise.

Automatically extract and visualize data lineage by mapping the flow of data from its origin to its destination. This comprehensive data lineage management solution gathers and assesses the lineage of critical data, illustrating the data flow and derivation rules from the source to the target. Understanding data lineage involves tracing the journey of data as it is processed, transformed, and utilized, thereby revealing the flow and derivation rules that govern it. The solution offers a multi-tier, column-level data lineage graph alongside a detailed list that tracks data progression from source to target. Users can drill down into data lineage at the business system, table, and column levels for a granular view. Additionally, it provides parsers for various environments to facilitate thorough analysis, including support for Big Data technologies. Utilizing our patented technology, the system conducts path-sensitive dynamic string analysis and data flow analysis within programs, enhancing the understanding of data movement. This capability ensures that organizations maintain a clear view of their data's journey, thereby fostering better data governance and compliance.

IBM Manta Data Lineage serves as a robust data lineage solution designed to enhance the transparency of data pipelines, enabling organizations to verify the accuracy of data throughout their models and systems. As companies weave AI into their operations and face increasing data complexity, the significance of data quality, provenance, and lineage continues to rise. Notably, IBM’s 2023 CEO study identified concerns regarding data lineage as the primary obstacle to the adoption of generative AI. To address these challenges, IBM provides an automated data lineage platform that effectively scans applications to create a detailed map of all data flows. This information is presented through an intuitive user interface (UI) and various other channels, catering to both technical experts and non-technical stakeholders. With IBM Manta Data Lineage, data operations teams gain extensive visibility and control over their data pipelines, enhancing their ability to manage data effectively. By deepening your understanding and utilization of dynamic metadata, you can guarantee that data is handled with precision and efficiency across intricate systems. This comprehensive approach not only mitigates risks but also fosters a culture of data-driven decision-making within organizations.

Blindata encompasses all the essential components of a comprehensive Data Governance program. Its features, including the Business Glossary, Data Catalog, and Data Lineage, work together to provide a cohesive and thorough perspective on your data. The Data Classification module assigns semantic significance to the data, while the Data Quality, Issue Management, and Data Stewardship modules enhance data reliability and foster trust. Additionally, specific functionalities for privacy compliance are available, such as a registry for processing activities, centralized management of privacy notes, and a consent registry that incorporates Blockchain for notarization. The Blindata Agent facilitates connections to various data sources, enabling the collection of metadata, including data structures like Tables, Views, and Fields, as well as data quality metrics and reverse lineage. With a modular design and fully API-driven architecture, Blindata supports seamless integration with vital business systems, including DBMS, Active Directory, e-commerce platforms, and various Data Platforms. This versatile solution can be deployed as a Software as a Service (SaaS), installed on-premises, or acquired through the AWS Marketplace, making it accessible for a wide range of organizational needs. Its flexibility ensures that businesses can tailor their Data Governance approach to meet specific requirements effectively.

Explore the exceptional features offered by Global IDs, which provide a comprehensive range of Enterprise Data Solutions including data governance, compliance, cloud migration, rationalization, privacy, analytics, and more. The Global IDs EDA Platform includes essential functionalities such as automated discovery and profiling, data classification, data lineage, and data quality, all aimed at ensuring that data is transparent, reliable, and understandable throughout the ecosystem. Additionally, the architecture of the Global IDs EDA platform is built for seamless integration, enabling access to all its functionalities through APIs. This platform effectively automates data management for organizations of varying sizes and diverse data environments. By utilizing Global IDs EDA, businesses can significantly enhance their data management practices and drive better decision-making.

Montara enables BI Teams and Data Analysts to model and transform data using SQL alone, easily and seamlessly, and enjoy benefits such a modular code, CI/CD and versioning, automated testing and documentation. With Montara, analysts are able to quickly understand the impact of changes in models on analysis, reports, and dashboards. Report-level lineage is supported, as well as support for 3rd-party visualization tools like Tableau and Looker. BI teams can also perform ad hoc analysis, create dashboards and reports directly on Montara.

The Collibra Data Intelligence Cloud serves as your comprehensive platform for engaging with data, featuring an exceptional catalog, adaptable governance, ongoing quality assurance, and integrated privacy measures. Empower your teams with a premier data catalog that seamlessly merges governance, privacy, and quality controls. Elevate efficiency by enabling teams to swiftly discover, comprehend, and access data from various sources, business applications, BI, and data science tools all within a unified hub. Protect your data's privacy by centralizing, automating, and streamlining workflows that foster collaboration, implement privacy measures, and comply with international regulations. Explore the complete narrative of your data with Collibra Data Lineage, which automatically delineates the connections between systems, applications, and reports, providing a contextually rich perspective throughout the organization. Focus on the most critical data while maintaining confidence in its relevance, completeness, and reliability, ensuring that your organization thrives in a data-driven world. By leveraging these capabilities, you can transform your data management practices and drive better decision-making across the board.

Arch Linux is a self-sufficient, x86-64 general-purpose GNU/Linux distribution that aims to deliver the most current stable software versions through a rolling-release approach. Its standard installation offers a bare-minimum base system, allowing users to customize their setup by installing only the necessary components. The philosophy of Arch Linux emphasizes simplicity, defined as the absence of unnecessary enhancements or alterations. The distribution provides software as originally released by upstream developers, minimizing downstream modifications, and avoiding patches that are not accepted upstream; any changes made are predominantly backported bug fixes that will be addressed in the next release. Furthermore, Arch includes the configuration files from upstream, with changes limited to specific distribution needs, such as modifying file paths within the system. Notably, Arch does not incorporate automation features like automatically enabling services upon package installation, thereby maintaining user control over their environment. This approach allows users to tailor their systems closely to their needs, fostering a deeper understanding and appreciation of their operating system.

Microsoft Purview serves as a comprehensive data governance platform that facilitates the management and oversight of your data across on-premises, multicloud, and software-as-a-service (SaaS) environments. With its capabilities in automated data discovery, sensitive data classification, and complete data lineage tracking, you can effortlessly develop a thorough and current representation of your data ecosystem. This empowers data users to access reliable and valuable data easily. The service provides automated identification of data lineage and classification across various sources, ensuring a cohesive view of your data assets and their interconnections for enhanced governance. Through semantic search, users can discover data using both business and technical terminology, providing insights into the location and flow of sensitive information within a hybrid data environment. By leveraging the Purview Data Map, you can lay the groundwork for effective data utilization and governance, while also automating and managing metadata from diverse sources. Additionally, it supports the classification of data using both predefined and custom classifiers, along with Microsoft Information Protection sensitivity labels, ensuring that your data governance framework is robust and adaptable. This combination of features positions Microsoft Purview as an essential tool for organizations seeking to optimize their data management strategies.

Creating and overseeing a thoroughly documented data project requires significant time and extensive manual coding, but that is no longer the case. We are confident in our ability to help you improve data transformation efficiency, and we can back that promise with results. Our column-aware architecture facilitates the reuse of data patterns and efficient change management on a large scale. By enhancing visibility around change management and impact analysis, we ensure safer and more predictable data operations. Coalesce offers specially curated packages containing best-practice templates that can automatically generate native-SQL for Snowflake™. If you have specific requirements, rest assured that our templates are fully customizable to suit your needs. Navigating through your data pipeline is a breeze with Coalesce, as every screen and button has been thoughtfully designed for easy access to all necessary tools. With Coalesce, your data team gains enhanced control over projects, allowing for features like side-by-side code comparison and immediate visibility into project and audit histories. Additionally, we guarantee that table-level and column-level lineage information is continuously updated and readily available, ensuring that your data remains accurate and reliable. Ultimately, Coalesce empowers your team to optimize workflows and focus on delivering insights rather than getting bogged down in administrative tasks.

K2View believes that every enterprise should be able to leverage its data to become as disruptive and agile as possible. We enable this through our Data Product Platform, which creates and manages a trusted dataset for every business entity – on demand, in real time. The dataset is always in sync with its sources, adapts to changes on the fly, and is instantly accessible to any authorized data consumer. We fuel operational use cases, including customer 360, data masking, test data management, data migration, and legacy application modernization – to deliver business outcomes at half the time and cost of other alternatives.

The contemporary data workspace transforms the accessibility of your data assets, making everything from data tables to BI reports easily discoverable. With our robust search algorithms and user-friendly browsing experience, locating the right asset becomes effortless. Atlan simplifies the identification of poor-quality data through the automatic generation of data quality profiles. This includes features like variable type detection, frequency distribution analysis, missing value identification, and outlier detection, ensuring you have comprehensive support. By alleviating the challenges associated with governing and managing your data ecosystem, Atlan streamlines the entire process. Additionally, Atlan’s intelligent bots analyze SQL query history to automatically construct data lineage and identify PII data, enabling you to establish dynamic access policies and implement top-notch governance. Even those without technical expertise can easily perform queries across various data lakes, warehouses, and databases using our intuitive query builder that resembles Excel. Furthermore, seamless integrations with platforms such as Tableau and Jupyter enhance collaborative efforts around data, fostering a more connected analytical environment. Thus, Atlan not only simplifies data management but also empowers users to leverage data effectively in their decision-making processes.

Talend Data Catalog provides your organization with a single point of control for all your data. Data Catalog provides robust tools for search, discovery, and connectors that allow you to extract metadata from almost any data source. It makes it easy to manage your data pipelines, protect your data, and accelerate your ETL process. Data Catalog automatically crawls, profiles and links all your metadata. Data Catalog automatically documents up to 80% of the data associated with it. Smart relationships and machine learning keep the data current and up-to-date, ensuring that the user has the most recent data. Data governance can be made a team sport by providing a single point of control that allows you to collaborate to improve data accessibility and accuracy. With intelligent data lineage tracking and compliance tracking, you can support data privacy and regulatory compliance.

The need for insights derived from data and for innovative solutions has reached unprecedented levels. In the current landscape of global business, maintaining a competitive advantage relies heavily on the capacity to utilize reliable data for making strategic and informed decisions. Sadly, despite the vast amounts of data that many organizations gather, it often goes underutilized because business leaders struggle to locate it or lack the understanding and trust necessary to leverage it effectively. ASG Data Intelligence (ASG DI) addresses this issue of data skepticism through its metadata-centric platform, which enhances the intelligence of technical data by providing comprehensive views of the data lifecycle and its transformations, alongside contextual business relevance. By empowering users across various roles—such as data scientists, analysts, and marketers—data can be harnessed to its full potential when it is accessible, comprehensible, and dependable. Establishing confidence in data is essential, and this is achieved by enhancing the understanding of its origins, the processes it undergoes, and the business context in which it operates. Consequently, organizations can transform their approach to data and drive greater innovation and efficiency.

SAP Information Steward software facilitates data profiling, monitoring, and the management of information policies. Acting as the information governance component of the SAP Business Technology Platform, it enables organizations to foresee risks and enhance business results. By integrating data profiling, data lineage, and metadata management, users can achieve ongoing visibility into the reliability of their enterprise data framework. This allows for a deeper comprehension of data quality throughout the data management ecosystem, while providing access to analytical metrics through user-friendly dashboards and scorecards. To advance enterprise information management efforts, it offers unwavering validation rules and guidelines to support analysts, data stewards, and IT professionals alike. With the ability to discover, evaluate, define, oversee, and enhance the quality of your enterprise data assets through data profiling and metadata management, all functions are available in a single solution. Moreover, organizations can simulate potential cost reductions stemming from enhanced data quality by conducting what-if analyses, thus paving the way for informed decision-making. Ultimately, this software not only streamlines processes but also reinforces the significance of maintaining high-quality data.

AI teams encounter obstacles that necessitate the development of innovative technologies, which we specialize in creating. Traditional data warehouses and lakes struggle to accommodate unstructured data types such as text, images, and videos. Our approach integrates AI with software development, specifically designed for data scientists, machine learning engineers, and data engineers alike. Instead of reinventing existing solutions, we provide a swift and cost-effective route to bring your projects into production. Your data remains securely stored under your control, and model training occurs on your own infrastructure. By addressing the limitations of current data handling methods, we ensure that AI teams can effectively meet their challenges. Our Studio functions as an extension of platforms like GitHub, GitLab, or BitBucket, allowing seamless integration. You can choose to sign up for our online SaaS version or reach out for an on-premise installation tailored to your needs. This flexibility allows organizations of all sizes to adopt our solutions effectively.

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.

SplineCloud serves as a collaborative knowledge management platform aimed at enhancing the identification, formalization, and sharing of structured and reusable knowledge within the realms of science and engineering. This innovative platform allows users to systematically arrange their data into organized repositories, ensuring that it is easily discoverable and accessible. Among its features are tools like an online plot digitizer, which helps in extracting data from graphical representations, and an interactive curve fitting tool, enabling users to establish functional relationships within datasets through the application of smooth spline functions. Additionally, users have the capability to incorporate datasets and relationships into their models and calculations by directly accessing them via the SplineCloud API or employing open source client libraries compatible with Python and MATLAB. By supporting the creation of reusable engineering and analytical applications, the platform aims to minimize design process redundancies, safeguard expert knowledge, and enhance decision-making efficiency. Ultimately, SplineCloud stands as a vital resource for researchers and engineers seeking to optimize their workflows and improve knowledge sharing in their fields.

DQOps is a data quality monitoring platform for data teams that helps detect and address quality issues before they impact your business. Track data quality KPIs on data quality dashboards and reach a 100% data quality score. DQOps helps monitor data warehouses and data lakes on the most popular data platforms. DQOps offers a built-in list of predefined data quality checks verifying key data quality dimensions. The extensibility of the platform allows you to modify existing checks or add custom, business-specific checks as needed. The DQOps platform easily integrates with DevOps environments and allows data quality definitions to be stored in a source repository along with the data pipeline code.

Ardent (available at tryardent.com) is a cutting-edge platform for AI data engineering that simplifies the building, maintenance, and scaling of data pipelines with minimal human input. Users can simply issue commands in natural language, while the system autonomously manages implementation, infers schemas, tracks lineage, and resolves errors. With its preconfigured ingestors, Ardent enables seamless connections to various data sources, including warehouses, orchestration systems, and databases, typically within 30 minutes. Additionally, it provides automated debugging capabilities by accessing web resources and documentation, having been trained on countless real engineering tasks to effectively address complex pipeline challenges without any manual intervention. Designed for production environments, Ardent adeptly manages numerous tables and pipelines at scale, executes parallel jobs, initiates self-healing workflows, and ensures data quality through monitoring, all while facilitating operations via APIs or a user interface. This unique approach not only enhances efficiency but also empowers teams to focus on strategic decision-making rather than routine technical tasks.

Dawiso is a comprehensive platform designed to simplify data management by integrating governance with usability for the entire organization. Central to Dawiso is its AI-powered data catalog, which empowers teams to quickly discover and understand trusted data across various systems, reports, and business applications. The platform’s flexible governance capabilities, alongside intuitive documentation apps, make it easy for both technical and non-technical users to collaborate effectively. Dawiso increases confidence in data through visual data lineage that clearly maps connections and dependencies across sources and systems. It supports regulatory compliance with customizable workflows, role-based access controls, and detailed metadata capture. By providing business-friendly tools and structured governance, Dawiso bridges communication gaps and streamlines data-driven decision-making. The platform promotes transparency, security, and usability in data management. Overall, Dawiso is built to enhance collaboration and trust in organizational data assets.

The Upstream Platform serves as a comprehensive cybersecurity and data management solution tailored for connected vehicles. By utilizing the data generated from these vehicles, it provides unmatched, mobility-focused actionable insights. Automotive original equipment manufacturers (OEMs), Tier 1 and Tier-2 suppliers, along with mobility service providers, utilize the Upstream Platform and its various applications to enhance vehicle security, optimize fleet management, improve business offerings, and generate revenue from vehicle data. This cloud-based platform processes connected vehicle information, ensuring it is normalized and cleansed, while also creating digital twins of vehicles and employing AI-driven detection to spot significant anomalies. These advanced features empower users with specialized and customizable applications for cybersecurity detection, response, and analytics across a range of scenarios. Use cases include exceptional cyber threat identification, multi-layered investigations, dynamic querying, comprehensive reporting, automated operational workflows, predictive maintenance strategies, insurance solutions, and much more, catering to a diverse array of industry needs.

Castor serves as a comprehensive data catalog aimed at facilitating widespread use throughout an entire organization. It provides a holistic view of your data ecosystem, allowing you to swiftly search for information using its robust search capabilities. Transitioning to a new data framework and accessing necessary data becomes effortless. This approach transcends conventional data catalogs by integrating various data sources, thereby ensuring a unified truth. With an engaging and automated documentation process, Castor simplifies the task of establishing trust in your data. Within minutes, users can visualize column-level, cross-system data lineage. Gain an overarching perspective of your data pipelines to enhance confidence in your data integrity. This tool enables users to address data challenges, conduct impact assessments, and ensure GDPR compliance all in one platform. Additionally, it helps in optimizing performance, costs, compliance, and security associated with your data management. By utilizing our automated infrastructure monitoring system, you can ensure the ongoing health of your data stack while streamlining data governance practices.

Bodo's robust computing engine, combined with its parallel processing methodology, ensures efficient performance and seamless scalability, accommodating over 10,000 cores and petabytes of data effortlessly. By utilizing standard Python APIs such as Pandas, Bodo accelerates the development process and simplifies maintenance for data science, data engineering, and machine learning tasks. Its bare-metal native code execution minimizes the risk of frequent failures, allowing users to identify and resolve issues before they reach the production stage through comprehensive end-to-end compilation. Experience the agility of experimenting with extensive datasets directly on your laptop, all while benefiting from the intuitive simplicity that Python offers. Moreover, you can create production-ready code without the complications of having to refactor for scalability across large infrastructures, thus streamlining your workflow significantly!