Metaphor - The Modern Metadata Platform
Hi HN, We are the team at Metaphor Data, backed by a16z & Amplify Partners. Today we are excited to announce the launch of our product Metaphor, a modern metadata platform that serves as a system of record for your organization's data ecosystem. Metaphor provides full visibility into your data landscape and empowers both data producers and consumers to work more effectively and efficiently. For producers, Metaphor enables quick and accurate impact analysis as well as offers critical insights like data consumption patterns and resource utilization. For consumers, it brings technical metadata…
What it does
In the maker’s words, at launch
Hi HN, We are the team at Metaphor Data, backed by a16z & Amplify Partners. Today we are excited to announce the launch of our product Metaphor, a modern metadata platform that serves as a system of record for your organization's data ecosystem. Metaphor provides full visibility into your data landscape and empowers both data producers and consumers to work more effectively and efficiently. For producers, Metaphor enables quick and accurate impact analysis as well as offers critical insights like data consumption patterns and resource utilization. For consumers, it brings technical metadata and business context together to inform decisions about how and when to use data products. To facilitate adoption, Metaphor seamlessly embeds these interactions directly in people's existing tools and workflows, such as Slack, Looker, and Notebooks, in addition to an easy-to-use web application. Metaphor has its root in DataHub, the leading open-source metadata platform project created by our team while working at LinkedIn. It shares the same architecture that has been battle-tested for the complexity and scale of industry leaders, such as LinkedIn, Expedia, Klarna, Peloton, DFDS, Saxo Bank, and Grofers. DataHub powered many metadata-related use cases at LinkedIn, including data discovery, GDPR/CCPA compliance, data integration, governance, and ML DevOps. After experiencing how DataHub supercharged the data democratization at LinkedIn, we decided to create Metaphor so that every company to realize the full potential of data through effective metadata management. The Modern Data Stack has helped democratize data across the company but also led to organic growth and fragmentation. This makes something as basic as finding the right data become increasingly more difficult—many data scientists & analysts spend as much as 30% of their time searching for a needle in the proverbial data haystack. Typical data catalogs fail to solve the data discovery and understandability problems as they rely solely on technical metadata. Metaphor took a different approach by combining three types of metadata: 1. Technical metadata ("What is it?"): All metadata sourced from the data systems, including schemas, lineage, SQL/code, description, data profile, data quality, etc. 2. Business metadata ("What do users call it?"): The mapping between physical data and business use cases governed at the company, organization, or team level. 3. Behavioral metadata ("Who, where and how is it being used?"): Linking data assets to the users who create, use, and depend on them, as well as the actual usage behavior. With this, Metaphor bridges the gap between the technical and business worlds and helps organizations to realize true data democratization on the Modern Data Stack. We have also written a blog post with more details and product screenshots here: https://metaphor.io/blog/metaphor-product-launch. You can check out a brief product demo video (5 mins): https://www.youtube.com/watch?v=MKPDjGwE0Ac We'd love the hear about your experience and challenges with this data discovery & metadata management space and any feedback/questions you have about what we are building.
Does the same job
all alternatives →
- ALA labelling tool to easily extract and label Wikipedia data2021 · ▲116
Hi HN! I am Maria, solo founder of DataQA (https://dataqa.ai/), a tool to search and label documents for various NLP tasks (e.g. entity extraction, entity linking, etc). I have worked as a data scientist and ML engineer for the better part of a decade, and over that time have specialised mainly in applications involving natural language processing (NLP). One of the key questions I have always had at the back of my mind is whether my time was well spent. Whenever I spent more time on feature engineering or trying different models, I always wondered whether I would get better…
More work this month
the category →
The app store for voice native apps that lives in your notch
Work · 28d ago · voiceos.com
The New Calendly▲211Handle all of the work before, during, and after meetings
Work · 17d ago · calendly.com
Launched alongside, November 2021
the whole month →
- MW
Life & fun · 2021 · nomdebebe.app
- IM
Dev tools · 2021 · apps.apple.com
Eligible startups get Intercom features at a 95% discount
Growth · 2021 · intercom.com
- LR
Life & fun · 2021 · jacquesmattheij.com
