{"id":5736,"url":"https://github.com/kelvins/awesome-dataops","name":"awesome-dataops","description":":sunglasses: A curated list of awesome DataOps tools","projects_count":144,"last_synced_at":"2026-08-11T02:00:19.610Z","repository":{"id":41087191,"uuid":"448318437","full_name":"kelvins/awesome-dataops","owner":"kelvins","description":":sunglasses: A curated list of awesome DataOps tools","archived":false,"fork":false,"pushed_at":"2025-12-10T23:09:13.000Z","size":173,"stargazers_count":235,"open_issues_count":4,"forks_count":38,"subscribers_count":7,"default_branch":"main","last_synced_at":"2026-07-22T16:03:19.059Z","etag":null,"topics":["awesome","awesome-list","data-engineer","data-engineering","dataops"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kelvins.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":null,"code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2022-01-15T15:40:17.000Z","updated_at":"2026-07-21T09:05:50.000Z","dependencies_parsed_at":"2024-01-12T08:48:08.838Z","dependency_job_id":"2c54293a-6e5c-40d3-97af-29a905f4d0e3","html_url":"https://github.com/kelvins/awesome-dataops","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/kelvins/awesome-dataops","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kelvins%2Fawesome-dataops","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kelvins%2Fawesome-dataops/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kelvins%2Fawesome-dataops/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kelvins%2Fawesome-dataops/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kelvins","download_url":"https://codeload.github.com/kelvins/awesome-dataops/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kelvins%2Fawesome-dataops/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":36501859,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-08-06T04:43:03.162Z","status":"online","status_checked_at":"2026-08-11T02:00:06.871Z","response_time":99,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-01-06T23:43:45.505Z","updated_at":"2026-08-11T02:00:19.613Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Other Lists","Data Ingestion","Data Warehouse","File System","Database","Data Quality","Data Exploration","Data Visualization","SQL Playground","SQL Query Engine","Books","Data Catalog","Data Processing","Metadata Service","Slack","Logging and Monitoring","Data Workflow","Data Serialization"],"sub_categories":["Vector Database","Data Table Format","Key-Value Database","Graph Database","Relational Database","Document-Oriented Database","Time Series Database","Columnar Database","Data Compression"],"readme":"# Awesome DataOps [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)\n\nA curated list of awesome DataOps tools.\n\n- [Awesome DataOps](#awesome-dataops)\n    - [Data Catalog](#data-catalog)\n    - [Data Exploration](#data-exploration)\n    - [Data Ingestion](#data-ingestion)\n    - [Data Processing](#data-processing)\n    - [Data Quality](#data-quality)\n    - [Data Serialization](#data-serialization)\n        - [Data Compression](#data-compression)\n        - [Data Table Format](#data-table-format)\n    - [Data Visualization](#data-visualization)\n    - [Data Warehouse](#data-warehouse)\n    - [Data Workflow](#data-workflow)\n    - [Database](#database)\n        - [Columnar Database](#columnar-database)\n        - [Document-Oriented Database](#document-oriented-database)\n        - [Graph Database](#graph-database)\n        - [Key-Value Database](#key-value-database)\n        - [Relational Database](#relational-database)\n        - [Time Series Database](#time-series-database)\n        - [Vector Database](#vector-database)\n    - [File System](#file-system)\n    - [Logging and Monitoring](#logging-and-monitoring)\n    - [Metadata Service](#metadata-service)\n    - [SQL Playground](#sql-playground)\n    - [SQL Query Engine](#sql-query-engine)\n- [Resources](#resources)\n    - [Books](#books)\n    - [Other Lists](#other-lists)\n    - [Slack](#slack)\n- [Contributing](#contributing)\n\n---\n\n## Data Catalog\n\n*Tools related to data cataloging.*\n\n* [Amundsen](https://www.amundsen.io/) - Data discovery and metadata engine for improving the productivity when interacting with data.\n* [Apache Atlas](https://atlas.apache.org) - Provides open metadata management and governance capabilities to build a data catalog.\n* [CKAN](https://github.com/ckan/ckan) - Open-source DMS (data management system) for powering data hubs and data portals.\n* [DataHub](https://github.com/linkedin/datahub) - LinkedIn's generalized metadata search \u0026 discovery tool.\n* [Magda](https://github.com/magda-io/magda) - A federated, open-source data catalog for all your big data and small data.\n* [Marquez](https://github.com/MarquezProject/marquez) - Service for the collection, aggregation, and visualization of a data ecosystem's metadata.\n* [Metacat](https://github.com/Netflix/metacat) - Unified metadata exploration API service for Hive, RDS, Teradata, Redshift, S3 and Cassandra.\n* [OpenLineage](https://github.com/OpenLineage/openlineage) - Open standard for metadata and lineage collection.\n* [OpenMetadata](https://open-metadata.org/) - A Single place to discover, collaborate and get your data right.\n* [Unity Catalog](https://www.unitycatalog.io/) - Industry’s only universal catalog for data and AI.\n\n## Data Exploration\n\n*Tools for performing data exploration.*\n\n* [Apache Zeppelin](https://zeppelin.apache.org/) - Enables data-driven, interactive data analytics and collaborative documents.\n* [Jupyter Notebook](https://jupyter.org/) - Web-based notebook environment for interactive computing.\n* [JupyterLab](https://jupyterlab.readthedocs.io) - The next-generation user interface for Project Jupyter.\n* [Jupytext](https://github.com/mwouts/jupytext) - Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts.\n* [Marimo](https://github.com/marimo-team/marimo) - A reactive Python notebook that's reproducible, git-friendly, and deployable as scripts or apps.\n* [Polynote](https://polynote.org/) - The polyglot notebook with first-class Scala support.\n\n## Data Ingestion\n\n*Tools for performing data ingestion.*\n\n* [Amazon Kinesis](https://aws.amazon.com/kinesis/) - Easily collect, process, and analyze video and data streams in real time.\n* [Apache Gobblin](https://github.com/apache/gobblin) - A framework that simplifies common aspects of big data such as data ingestion.\n* [Apache Kafka](https://github.com/apache/kafka) - Open-source distributed event streaming platform used by thousands of companies.\n* [Apache Pulsar](https://github.com/apache/pulsar) - Distributed pub-sub messaging platform with a flexible messaging model and intuitive API.\n* [Embulk](https://github.com/embulk/embulk) - A parallel bulk data loader that helps data transfer between various storages.\n* [Fluentd](https://github.com/fluent/fluentd) - Collects events from various data sources and writes them to files.\n* [Google PubSub](https://cloud.google.com/pubsub) - Ingest events for streaming into BigQuery, data lakes or operational databases.\n* [Nakadi](https://github.com/zalando/nakadi) - A distributed event bus that implements a RESTful API abstraction on top of Kafka-like queues.\n* [Pravega](https://github.com/pravega/pravega) - An open source distributed storage service implementing Streams.\n* [RabbitMQ](https://www.rabbitmq.com/) - One of the most popular open source message brokers.\n\n## Data Workflow\n\n*Tools related to data workflow/pipeline.*\n\n* [Apache Airflow](https://github.com/apache/airflow) - A platform to programmatically author, schedule, and monitor workflows.\n* [Apache Oozie](https://github.com/apache/oozie) - An extensible, scalable and reliable system to manage complex Hadoop workloads.\n* [Azkaban](https://github.com/azkaban/azkaban) - Batch workflow job scheduler created at LinkedIn to run Hadoop jobs.\n* [Dagster](https://github.com/dagster-io/dagster) - An orchestration platform for the development, production, and observation of data assets.\n* [Luigi](https://github.com/spotify/luigi) - Python module that helps you build complex pipelines of batch jobs.\n* [Prefect](https://docs.prefect.io/) - A workflow management system, designed for modern infrastructure.\n\n## Data Processing\n\n*Tools related to data processing (batch and stream).*\n\n* [Apache Beam](https://github.com/apache/beam) - A unified model for defining both batch and streaming data-parallel processing pipelines.\n* [Apache Flink](https://github.com/apache/flink) - An open source stream processing framework with powerful capabilities.\n* [Apache Hadoop MapReduce](https://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html) - A framework for writing applications which process vast amounts of data.\n* [Apache Nifi](https://github.com/apache/nifi) - An easy to use, powerful, and reliable system to process and distribute data.\n* [Apache Samza](https://github.com/apache/samza) - A distributed stream processing framework which uses Apache Kafka and Hadoop YARN.\n* [Apache Spark](https://github.com/apache/spark) - A unified analytics engine for large-scale data processing.\n* [Apache Storm](https://github.com/apache/storm) - An open source distributed realtime computation system.\n* [Apache Tez](https://github.com/apache/tez) - A generic data-processing pipeline engine envisioned as a low-level engine.\n* [Faust](https://github.com/robinhood/faust) - A stream processing library, porting the ideas from Kafka Streams to Python.\n* [skrub](http://skrub-data.org) - Python library to ease preprocessing and feature engineering for tabular machine learning.\n\n## Data Quality\n\n*Tools for ensuring data quality.*\n\n* [Cerberus](https://github.com/pyeve/cerberus) - Lightweight, extensible data validation library for Python.\n* [Cleanlab](https://github.com/cleanlab/cleanlab) - Data-centric AI tool to detect (non-predefined) issues in ML data like label errors or outliers.\n* [DataProfiler](https://github.com/capitalone/DataProfiler) - A Python library designed to make data analysis, monitoring, and sensitive data detection easy.\n* [Deequ](https://github.com/awslabs/deequ) - A library built on top of Apache Spark for measuring data quality in large datasets.\n* [Great Expectations](https://greatexpectations.io) - A Python data validation framework that allows to test your data against datasets.\n* [JSON Schema](https://json-schema.org/) - A vocabulary that allows you to annotate and validate JSON documents.\n* [SodaSQL](https://github.com/sodadata/soda-sql) - Data profiling, testing, and monitoring for SQL accessible data.\n\n## Data Serialization\n\n*Tools related to data serialization.*\n\n* [Apache Avro](https://github.com/apache/avro) - A data serialization system which is compact, fast and provides rich data structures.\n* [Apache ORC](https://github.com/apache/orc) - A self-describing type-aware columnar file format designed for Hadoop workloads.\n* [Apache Parquet](https://github.com/apache/parquet-mr) - A columnar storage format which provides efficient storage and encoding of data.\n* [Kryo](https://github.com/EsotericSoftware/kryo) - A fast and efficient binary object graph serialization framework for Java.\n* [ProtoBuf](https://github.com/protocolbuffers/protobuf) - Language-neutral, platform-neutral, extensible mechanism for serializing structured data.\n\n### Data Compression\n\n* [Pigz](https://github.com/madler/pigz) - A parallel implementation of gzip for modern multi-processor, multi-core machines.\n* [Snappy](https://github.com/google/snappy) - Open source compression library that is fast, stable and robuts.\n\n### Data Table Format\n\n* [Apache Hudi](https://github.com/apache/hudi) - Manages the storage of large analytical datasets on DFS.\n* [Apache Iceberg](https://github.com/apache/iceberg) - Open table format for huge analytic datasets.\n* [Delta Lake](https://github.com/delta-io/delta) - An open source project that enables building a Lakehouse architecture on top of data lakes.\n\n## Data Visualization\n\n*Tools for performing data visualization (DataViz).*\n\n* [Apache Superset](https://github.com/apache/superset) - A modern data exploration and data visualization platform.\n* [Count](https://count.co) - SQL/drag-and-drop querying and visualisation tool based on notebooks.\n* [Dash](https://github.com/plotly/dash) - Analytical Web Apps for Python, R, Julia, and Jupyter.\n* [Data Studio](https://datastudio.google.com) - Reporting solution for power users who want to go beyond the data and dashboards of GA.\n* [HUE](https://github.com/cloudera/hue) - A mature SQL Assistant for querying Databases \u0026 Data Warehouses.\n* [Lux](https://github.com/lux-org/lux) - Fast and easy data exploration by automating the visualization and data analysis process.\n* [Metabase](https://www.metabase.com/) - The simplest, fastest way to get business intelligence and analytics to everyone.\n* [Redash](https://redash.io/) - Connect to any data source, easily visualize, dashboard and share your data.\n* [Tableau](https://www.tableau.com) - Powerful and fastest growing data visualization tool used in the business intelligence industry.\n\n## Data Warehouse\n\n*Tools related to storing data in data warehouses (DW).*\n\n* [Amazon Redshift](https://aws.amazon.com/redshift/) - Accelerate your time to insights with fast, easy, and secure cloud data warehousing.\n* [Apache Hive](https://github.com/apache/hive) - Facilitates reading, writing, and managing large datasets residing in distributed storage.\n* [Apache Kylin](https://github.com/apache/kylin) - An open source, distributed analytical data warehouse for big data.\n* [Google BigQuery](https://cloud.google.com/bigquery) - Serverless, highly scalable, and cost-effective multicloud data warehouse.\n\n## Database\n\n*Database tools for storing data.*\n\n### Columnar Database\n\n* [Apache Cassandra](https://github.com/apache/cassandra) - Open source column based DBMS designed to handle large amounts of data.\n* [Apache Druid](https://github.com/apache/druid) - Designed to quickly ingest massive quantities of event data, and provide low-latency queries.\n* [Apache HBase](https://github.com/apache/hbase) - An open-source, distributed, versioned, column-oriented store.\n* [Scylla](https://github.com/scylladb/scylla) - Designed to be compatible with Cassandra while achieving higher throughputs and lower latencies.\n\n### Document-Oriented Database\n\n* [Apache CouchDB](https://github.com/apache/couchdb) - An open-source document-oriented NoSQL database, implemented in Erlang.\n* [Elasticsearch](https://github.com/elastic/elasticsearch) - A distributed document oriented database with a RESTful search engine.\n* [MongoDB](https://github.com/mongodb/mongo) - A cross-platform document database that uses JSON-like documents with optional schemas.\n* [RethinkDB](https://github.com/rethinkdb/rethinkdb) - The first open-source scalable database built for realtime applications.\n\n### Graph Database\n\n* [Age](https://github.com/apache/age) - A multi-model database that supports both graph and relational data models.\n* [ArangoDB](https://github.com/arangodb/arangodb) - A scalable open-source multi-model database natively supporting graph, document and search.\n* [JanusGraph](https://github.com/JanusGraph/janusgraph) - Manage large graphs with billions of data distributed across a multi-machine cluster.\n* [Memgraph](https://github.com/memgraph/memgraph) - An open source graph database, built for real-time streaming data, compatible with Neo4j.\n* [Neo4j](https://github.com/neo4j/neo4j) - A high performance graph store with all the features expected of a mature and robust database.\n* [Titan](https://github.com/thinkaurelius/titan) - A highly scalable graph database optimized for storing and querying large graphs.\n\n### Key-Value Database\n\n* [Apache Accumulo](https://github.com/apache/accumulo) - A sorted, distributed key-value store that provides robust and scalable data storage.\n* [Dragonfly](https://github.com/dragonflydb/dragonfly) - A modern in-memory datastore, fully compatible with Redis and Memcached APIs.\n* [DynamoDB](https://aws.amazon.com/dynamodb/) - Fast, flexible NoSQL database service for single-digit millisecond performance at any scale.\n* [etcd](https://github.com/etcd-io/etcd) - Distributed reliable key-value store for the most critical data of a distributed system.\n* [EVCache](https://github.com/Netflix/EVCache) - A distributed in-memory data store for the cloud.\n* [Memcached](https://github.com/memcached/memcached) - A high performance multithreaded event-based key/value cache store.\n* [Redis](https://github.com/redis/redis) - An in-memory key-value database that persists on disk.\n\n### Relational Database\n\n* [CockroachDB](https://github.com/cockroachdb/cockroach) - A distributed database designed to build, scale, and manage data-intensive apps.\n* [Crate](https://github.com/crate/crate) - A distributed SQL database that makes it simple to store and analyze massive amounts of data.\n* [MariaDB](https://github.com/MariaDB/server) - A replacement of MySQL with more features, new storage engines and better performance.\n* [MySQL](https://github.com/mysql/mysql-server) - One of the most popular open source transactional databases.\n* [PostgreSQL](https://github.com/postgres/postgres) - An advanced RDBMS that supports an extended subset of the SQL standard.\n* [RQLite](https://github.com/rqlite/rqlite) - A lightweight, distributed relational database, which uses SQLite as its storage engine.\n* [SQLite](https://github.com/sqlite/sqlite) - A popular choice as embedded database software for local/client storage.\n\n### Time Series Database\n\n* [Akumuli](https://github.com/akumuli/Akumuli) - Can be used to capture, store and process time-series data in real-time.\n* [Atlas](https://github.com/Netflix/Atlas) - An in-memory dimensional time series database.\n* [InfluxDB](https://github.com/influxdata/influxdb) - Scalable datastore for metrics, events, and real-time analytics.\n* [QuestDB](https://github.com/questdb/questdb) - An open source SQL database designed to process time series data, faster.\n* [TimescaleDB](https://github.com/timescale/timescaledb) - Open-source time-series SQL database optimized for fast ingest and complex queries.\n\n### Vector Database\n\n* [Milvus](https://github.com/milvus-io/milvus/) - An open source embedding vector similarity search engine powered by Faiss, NMSLIB and Annoy.\n* [Pinecone](https://www.pinecone.io) - Managed and distributed vector similarity search used with a lightweight SDK.\n* [Qdrant](https://github.com/qdrant/qdrant) - An open source vector similarity search engine with extended filtering support.\n\n## File System\n\n*Tools related to file system and data storage.*\n\n* [Alluxio](https://github.com/Alluxio/alluxio) - A virtual distributed storage system.\n* [Amazon Simple Storage Service (S3)](https://aws.amazon.com/s3/) - Object storage built to retrieve any amount of data from anywhere.\n* [Apache Hadoop Distributed File System (HDFS)](https://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/HdfsDesign.html) - A distributed file system.\n* [GlusterFS](https://github.com/gluster/glusterfs) - A software defined distributed storage that can scale to several petabytes.\n* [Google Cloud Storage (GCS)](https://cloud.google.com/storage) - Object storage for companies of all sizes, to store any amount of data.\n* [LakeFS](https://github.com/treeverse/lakeFS) - Open source tool that transforms your object storage into a Git-like repository.\n* [LizardFS](https://github.com/lizardfs/lizardfs) - A highly reliable, scalable and efficient distributed file system.\n* [MinIO](https://github.com/minio/minio) - High Performance, Kubernetes Native Object Storage compatible with Amazon S3 API.\n* [SeaweedFS](https://github.com/chrislusf/seaweedfs) - A fast distributed storage system for blobs, objects, files, and data lake.\n* [Swift](https://github.com/openstack/swift) - A distributed object storage system designed to scale from a single machine to thousands of servers.\n\n## Logging and Monitoring\n\n*Tools used for logging and monitoring data workflows.*\n\n* [Grafana](https://github.com/grafana/grafana) - Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, InfluxDB and more.\n* [Loki](https://github.com/grafana/loki) - A horizontally-scalable, highly-available, multi-tenant log aggregation system inspired by Prometheus.\n* [Prometheus](https://github.com/prometheus/prometheus) - A monitoring system and time series database.\n* [Whylogs](https://github.com/whylabs/whylogs) - A tool for creating data logs, enabling monitoring for data drift and data quality issues.\n\n## Metadata Service\n\n*Tools used for storing and serving metadata.*\n\n* [Hive Metastore](https://cwiki.apache.org/confluence/display/hive/design#Design-Metastore) - Service that stores metadata related to Apache Hive and other services.\n* [Metacat](https://github.com/Netflix/metacat) - Provides you information about what data you have, where it resides and how to process it.\n\n## SQL Playground\n\n*Tools for testing and sharing SQL snippets in mock databases.*\n\n* [RunSQL](https://runsql.com/) - Free online SQL playground for MySQL, PostgreSQL, and SQL Server.\n* [SQLFiddle](https://sqlfiddle.com/) - Online SQL compiler for learning and practicing SQL.\n\n## SQL Query Engine\n\n*Tools for parallel processing SQL statements.*\n\n* [Apache Drill](https://github.com/apache/drill) - Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage.\n* [Apache Impala](https://github.com/apache/impala) - Lightning-fast, distributed SQL queries for petabytes of data.\n* [Dremio](https://www.dremio.com/) - Power high-performing BI dashboards and interactive analytics directly on data lake.\n* [Presto](https://github.com/prestodb/presto) - A distributed SQL query engine for big data.\n* [Trino](https://github.com/trinodb/trino) - A fast distributed SQL query engine for big data analytics.\n\n---\n\n# Resources\n\nWhere to discover new tools and discuss about existing ones.\n\n## Books\n\n* [Data Mesh: Delivering Data-Driven Value at Scale](https://www.oreilly.com/library/view/data-mesh/9781492092384/) (O'Reilly)\n* [Designing Data-Intensive Applications](https://www.oreilly.com/library/view/designing-data-intensive-applications/9781491903063/) (O'Reilly)\n* [Fundamentals of Data Engineering](https://www.oreilly.com/library/view/fundamentals-of-data/9781098108298/) (O'Reilly)\n* [Getting Started with Impala](https://www.oreilly.com/library/view/getting-started-with/9781491905760/) (O'Reilly)\n* [Learning and Operating Presto](https://www.oreilly.com/library/view/learning-and-operating/9781098141844/) (O'Reilly)\n* [Learning Spark: Lightning-Fast Data Analytics](https://www.oreilly.com/library/view/learning-spark-2nd/9781492050032/) (O'Reilly)\n* [Spark in Action](https://www.oreilly.com/library/view/spark-in-action/9781617295522/) (O'Reilly)\n* [Spark: The Definitive Guide](https://www.oreilly.com/library/view/spark-the-definitive/9781491912201/) (O'Reilly)\n\n## Other Lists\n\n* [Awesome Data Engineering](https://github.com/igorbarinov/awesome-data-engineering)\n* [Awesome MLOps](https://github.com/kelvins/awesome-mlops)\n* [DataOps Resource](https://github.com/chen1649chenli/dataOpsResource)\n\n## Slack\n\n* [Delta Lake Workspace](https://delta-users.slack.com/ssb/redirect)\n* [Trino Workspace](https://trinodb.slack.com/ssb/redirect)\n\n---\n\n# Contributing\n\nAll contributions are welcome! Please take a look at the [contribution guidelines](https://github.com/kelvins/awesome-dataops/blob/main/CONTRIBUTING.md) first.\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/kelvins%2Fawesome-dataops/projects"}