New Product Announcements Expand MongoDB’s Data Leadership Beyond the Database
MongoDB, Inc. (NASDAQ: MDB), the leading, modern general purpose data platform, today announced new cloud services and features that will provide a better way to work with data beyond the database. The Beta versions of MongoDB Atlas Data Lake and MongoDB Atlas Full-Text Search allow users to access compelling new features in a fully managed MongoDB environment with no additional infrastructure or systems to manage. Furthermore, the general availability of MongoDB Charts lets customers create charts and graphs, build and share dashboards and embed them directly into web apps to create more engaging user experiences.
MongoDB Atlas Data Lake allows customers to quickly query data on S3 in any format, including JSON, BSON, CSV, TSV, Parquet and Avro, using the MongoDB Query Language (MQL). Full-Text Search gives end users the flexibility to filter, rank and sort through their data to quickly surface the most relevant results, without having to pair their database with an external search engine and be forced to learn, scale, manage and support two entirely separate systems. MongoDB Charts is generally available to both Atlas and on-premises customers, providing them with the fastest and easiest way to create real-time visualizations of MongoDB data.
“Our new offerings radically expand the ways developers can use MongoDB to better work with data,” said Dev Ittycheria, CEO and President, MongoDB. “We strive to help developers be more productive and remove infrastructure headaches --- with additional features along with adjunct capabilities like full-text search and data lake. IDC predicts that by 2025 global data will reach 175 Zettabytes and 49% of it will reside in the public cloud. It’s our mission to give developers better ways to work with data wherever it resides, including in public and private clouds.”
Extend the Popularity of MQL
Developers love the MongoDB Query Language because it is so expressive, allowing them to query data any way they want – from simple lookups and range queries to creating sophisticated processing pipelines for data analytics and transformations, JOINs, geospatial processing and graph traversals. By bringing the MongoDB Query Language to the MongoDB Atlas Data Lake, developers can now use that same query language across data on S3, making querying massive data sets easy and cost-effective.
Give Developers the Best Data Lake for Cloud
More and more data is being stored in the cloud. However, the complexity of Hadoop and the rigidity of traditional data warehouses make it increasingly difficult and expensive to get value from rich, modern data in the cloud.
With MongoDB Atlas Data Lake, customers simply need to provide access to existing S3 storage buckets with a few clicks from the MongoDB Atlas console and they can run queries and explore their data using the power of MQL. Atlas Data Lake is completely serverless, so there is no infrastructure to set up, manage or optimize, and customers pay only for the queries they run when actively working with the data. Availability of MongoDB Atlas Data Lake on Google Cloud Storage and Azure Storage is planned for the future.
Remove Search Complexity with Atlas Full-Text Search
Atlas Full-Text Search provides rich text search capabilities based on Apache Lucene 8 against fully managed MongoDB databases with no additional infrastructure or systems to manage. Once indexes have been created using either the Atlas UI or API, developers can run sophisticated search queries using MQL, saving significant effort, time, and money.
Visualize MongoDB Data Natively
Visualizing data using charts, maps and dashboards is essential in order to unlock business insights from massive data collections and make them accessible to users. Available as a managed service in MongoDB Atlas, or downloadable to run on-premises, MongoDB Charts includes powerful new features, such as:
- Embedded charts in external web applications
- Geospatial data visualization with new map charts
- Built-in workload isolation to eliminate the impact of analytics queries on an operational application