Okyline is an Executable Data Design (EDD) platform for declarative data validation contracts and measurable operational data quality.
Instead of maintaining disconnected specifications, validators, tests, and quality dashboards, Okyline uses a single executable contract as the operational source of truth for validation and flow quality monitoring.
The same readable contract drives multi-format validation, deterministic execution, quality measurement, data quality gate, and historical quality analytics across APIs, events, files, LLM structured outputs, and enterprise data flows.
Community Edition provides the open specification, a free Java validation runtime, a public Claude AI assistant for contract generation, and a free online studio for executable JSON validation contracts and JSON Schema transpilation.
Enterprise Edition supports direct validation of JSONL, XML, CSV, FIXED, and EDI flows, data quality gate, and operational quality dashboards, all without databases
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SCIKIQ Data Fabric makes enterprise data AI-ready, without rebuilding the data stack, In weeks and months or years SCIKIQ is an AI-native Data & Intelligence Platform that helps enterprises connect, contextualize, govern and activate their data for analytics, Generative AI and intelligent agents. Instead of adding another disconnected tool, SCIKIQ creates a unified intelligence layer across the technology you already use from SAP, Oracle and Salesforce to Snowflake, Databricks, cloud platforms, data lakes and enterprise applications.
The result is trusted, contextualized and AI-ready enterprise data — in weeks, not years. What makes SCIKIQ Data Fabric different is its ability to bring the entire data-to-AI journey into one platform. Data integration, transformation, data quality, governance, catalog, lineage, semantic models, knowledge graphs, conversational analytics, AI/ML, data products and AI agents work together rather than as separate tools.
At the heart of SCIKIQ is Contextual Intelligence. SCIKIQ connects technical metadata with business definitions, KPIs, ownership, relationships and rules so that people and AI understand what enterprise data actually means. This semantic foundation helps create more trusted analytics and better-grounded AI responses.
Business users can simply ask questions of their enterprise data in natural language, explore KPIs and root causes, and receive contextual answers without depending on SQL or waiting for another report.
For data and technology teams, SCIKIQ provides a governed foundation with 200+ connectors, active metadata, multi-hop lineage, data quality, role-based governance and multi-cloud support across AWS, Azure, GCP, hybrid and on-prem environments, and you don't have to rip and replace your existing investments. SCIKIQ works with your stack, not against it.
SCIKIQ has been recognized by Forrester, NASSCOM, YourStory, Inc42 and DataIQ, providing independent validation of its innovation in enterprise data and AI.
If your enterprise already has data but is struggling to turn it into trusted AI, SCIKIQ is where that journey begins
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Apache Doris
Apache Doris is a modern data warehouse for real-time analytics. It delivers lightning-fast analytics on real-time data at scale.
Push-based micro-batch and pull-based streaming data ingestion within a second. Storage engine with real-time upsert, append and pre-aggregation.
Optimize for high-concurrency and high-throughput queries with columnar storage engine, MPP architecture, cost based query optimizer, vectorized execution engine.
Federated querying of data lakes such as Hive, Iceberg and Hudi, and databases such as MySQL and PostgreSQL.
Compound data types such as Array, Map and JSON. Variant data type to support auto data type inference of JSON data. NGram bloomfilter and inverted index for text searches.
Distributed design for linear scalability. Workload isolation and tiered storage for efficient resource management. Supports shared-nothing clusters as well as separation of storage and compute.
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