MongoDB Atlas Data Lake is a new service offered by MongoDB Atlas. Many organizations store long term, archival data in cost-effective storage like S3, GCP, and Azure Blobs. However, many of them do not have robust systems or tools to effectively utilize large amounts of data to inform decision making. MongoDB Atlas Data Lake is a service allowing organizations to analyze their long-term data to discover a wealth of information about their business.
This session will take a deep dive into the features that are currently available in MongoDB Atlas Data Lake and how they are implemented. In addition, we'll discuss future plans and opportunities and offer ample Q&A time with the engineers on the project.
2. #MDBLocal
State of Affairs
Businesses have a humongous amount of data
• IDC predicts that by 2025 global data will reach 175 Zettabytes and 49% of it will reside in the
public cloud.
Cloud storage is cost-effective
Cloud storage is hard to operationalize
3. #MDBLocal
A New Service Offered by MongoDB Atlas
Access long-term data
Query long-term data
Analyze long-term data
4. #MDBLocal
Requirements
Look and act like MongoDB
Access customer’s data securely
Handle queries over vast amounts of data
Handle long-running queries
Efficient use of resources
6. #MDBLocal
Language
Must be able to communicate with our drivers
Written in Go
Implemented a TCP server
Used mongo-go-driver’s wireprotocol package
Used mongo-go-driver's bson package
7. #MDBLocal
Security
Must have the same security as MongoDB
Users configured in Atlas
Implemented MongoDB’s security model
Require the use of TLS + SNI(Server Name Indicator)
31. Our Developer focused talks
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