Purpose-built databases to meet the challenges of the new era of business
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Before the web and cloud revolutions, business data looked like the skit of the old SNL show. The track was set in a Chicago Loop restaurant, a place where you could get anything you wanted — as long as it was a cheeseburger.
It's something like the database market before the Web. The answer to each database question was: "a relational database". They usually came from Oracle, Microsoft or IBM.
New varieties have emerged with the emergence of purpose-built NoSQL databases. Today, new competitors are always arriving, usually with a particular focus on cloud architecture. Previous entries evolve offerings, often bringing familiar elements of relational technology.
A world where data mostly took the form of entries in corporate ledgers turned into one with data of all kinds. This included data ranging from online user tracking activity to machine operation event logs, and more.
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Register nowAmong the purpose-built databases that have topped DB-Engines' top 10 rankings to join Oracle, Microsoft, and IBM in popularity include MongoDB (#5), which began life as a database document-oriented; Redis (#6), originally an in-memory key-value store; and Elasticsearch (#7), a search engine that has adopted many database styles.
Many other databases continue to make the top 10. This story looks at three of them.
Pinecone Systems - Machine Learning Generates Vector DatabasePinecone Systems, Inc. sort of grew out of AWS, one of the hotbeds of artificial intelligence (AI) and large-scale machine learning (ML). Pinecone CEO and Founder Edo Liberty previously ran Amazon AI Labs.
ML work at Amazon and elsewhere has introduced a new type of data, vector data, into an already dynamic data mix. Pinecone is part of a small group - including Milvus, Zilliz and others - bringing vector data platforms to a global market for ML that is expected to grow from $17 billion in 2021 to $90 billion by here 2026.
Basically, vector embeddings result from work on deep learning neural network models that convert raw data into simpler object vectors that can be used in a variety of applications. Cloud houses like AWS have felt the need to store, find, and manage these integrations as part of their operations.
Such work is difficult for organizations with limited resources outside the ranks of the big cloud giants. This gave impetus to Pinecone's distributed team located in New York, San Francisco and Tel Aviv.
As of 2019, the company has been working to create a vector database specifically designed to handle vector embeddings produced by machine learning. Pinecone cites users such as Clubhouse, Expel, CourseHero and others. Use cases include cyber threat detection, document duplication, personalized article recommendations, and semantic search. The company recently launched keyword-aware semantic search capabilities based on its vector database.
Greg Kogan, vice president of marketing at Pinecone, said the company's goal is to help use vector output from the AI model in production....
Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and gain efficiencies by improving and scaling citizen developers. Watch now.
Before the web and cloud revolutions, business data looked like the skit of the old SNL show. The track was set in a Chicago Loop restaurant, a place where you could get anything you wanted — as long as it was a cheeseburger.
It's something like the database market before the Web. The answer to each database question was: "a relational database". They usually came from Oracle, Microsoft or IBM.
New varieties have emerged with the emergence of purpose-built NoSQL databases. Today, new competitors are always arriving, usually with a particular focus on cloud architecture. Previous entries evolve offerings, often bringing familiar elements of relational technology.
A world where data mostly took the form of entries in corporate ledgers turned into one with data of all kinds. This included data ranging from online user tracking activity to machine operation event logs, and more.
EventSmart Security Summit
Learn about the essential role of AI and ML in cybersecurity and industry-specific case studies on December 8. Sign up for your free pass today.
Register nowAmong the purpose-built databases that have topped DB-Engines' top 10 rankings to join Oracle, Microsoft, and IBM in popularity include MongoDB (#5), which began life as a database document-oriented; Redis (#6), originally an in-memory key-value store; and Elasticsearch (#7), a search engine that has adopted many database styles.
Many other databases continue to make the top 10. This story looks at three of them.
Pinecone Systems - Machine Learning Generates Vector DatabasePinecone Systems, Inc. sort of grew out of AWS, one of the hotbeds of artificial intelligence (AI) and large-scale machine learning (ML). Pinecone CEO and Founder Edo Liberty previously ran Amazon AI Labs.
ML work at Amazon and elsewhere has introduced a new type of data, vector data, into an already dynamic data mix. Pinecone is part of a small group - including Milvus, Zilliz and others - bringing vector data platforms to a global market for ML that is expected to grow from $17 billion in 2021 to $90 billion by here 2026.
Basically, vector embeddings result from work on deep learning neural network models that convert raw data into simpler object vectors that can be used in a variety of applications. Cloud houses like AWS have felt the need to store, find, and manage these integrations as part of their operations.
Such work is difficult for organizations with limited resources outside the ranks of the big cloud giants. This gave impetus to Pinecone's distributed team located in New York, San Francisco and Tel Aviv.
As of 2019, the company has been working to create a vector database specifically designed to handle vector embeddings produced by machine learning. Pinecone cites users such as Clubhouse, Expel, CourseHero and others. Use cases include cyber threat detection, document duplication, personalized article recommendations, and semantic search. The company recently launched keyword-aware semantic search capabilities based on its vector database.
Greg Kogan, vice president of marketing at Pinecone, said the company's goal is to help use vector output from the AI model in production....
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