Tokern Description
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.
Tokern Alternatives
AnalyticsCreator is a metadata-driven design application for data warehouse automation and data product engineering across the Microsoft data stack.
Its Governed Control Model connects business meaning, data structures, transformation rules, dependencies, lineage and technical implementation in one controlled project model. Data teams design the required architecture in AnalyticsCreator, then generate native Microsoft assets from that design.
Generated outputs can include SQL Server objects, SSIS packages, Azure Data Factory pipelines, supported Microsoft Fabric components, deployment artefacts and Power BI semantic models. AnalyticsCreator supports dimensional, 3NF and hybrid modelling approaches together with ingestion, transformations, delta loading, historisation, Slowly Changing Dimensions, snapshots and repeatable data-processing patterns.
Because generated outputs are native Microsoft technology, no AnalyticsCreator runtime is required in production. Organisations retain ownership of the resulting implementation and can integrate generated assets into Git, Azure DevOps and CI/CD workflows.
Lineage, documentation and dependency information remain connected to the design, helping teams understand change impact before regenerating affected assets.
Design Intelligence extends this governed project context into AI-assisted data engineering by providing authorised AI tools and agents with structured access to metadata, lineage, dependencies and design rules.
Typical use cases include enterprise data warehouse development, Microsoft Fabric adoption, SQL Server and SSIS modernisation, governed Power BI delivery and repeatable data product engineering.
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SCIKIQ is one of the most innovative AI-native Data & Intelligence platforms for enterprises, built to make enterprise data AI-ready in weeks, not years.
Recognized by Forrester among leading AI-augmented data platforms, NASSCOM League of 10, YourStory Tech30, Inc42 and DataIQ, SCIKIQ is trusted by leading global enterprises across the USA, India, and UAE.
SCIKIQ brings Data Integration, Data Quality, Data Governance, Metadata Management, Data Lineage, Semantic Intelligence, Knowledge Graphs, Conversational Analytics, Generative AI, Data Products and AI Agents together in one unified platform. Unlike traditional data platforms that require enterprises to move or rebuild their technology stack, SCIKIQ works with what you already have. Connect SAP, Salesforce, Oracle, Snowflake, Databricks, AWS, Azure, GCP, data lakes, warehouses and enterprise applications through 200+ pre-built connectors, with no rip-and-replace.
What makes SCIKIQ different is Contextual Intelligence.
SCIKIQ doesn't just connect data; it helps AI understand its business meaning. Its semantic layer combines business terms, KPI definitions, metadata, lineage, ownership, rules, ontologies and relationships to create a trusted foundation for enterprise AI. Business users can talk to their data in natural language, investigate KPIs, discover root causes and generate insights without SQL. Data teams gain enterprise-grade governance, quality, lineage and control. AI teams get trusted, contextual data for building GenAI applications and intelligent AI agents.
Why enterprises choose SCIKIQ
AI-ready in 3–6 weeks | 167+ connectors | 99.9% availability | Multi-cloud | No-code | No vendor lock-in | No replatforming
Proven production deployments across Manufacturing retail, airlines, logistics, BFSI
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Kylo
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.
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Atlan
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.
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Company Details
Company:
Tokern
Year Founded:
2019
Headquarters:
United States
Website:
tokern.io
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