Securely integrate MongoDB Atlas with Amazon Kinesis
About MongoDB Atlas
MongoDB Atlas is a cloud database service for applications that works with Amazon AWS, Microsoft Azure, and Google Cloud Platform. The database service seeks to comply with the most stringent data security and privacy standards while offering a reliable suite of drivers, tools, and integrations. By automating numerous database management tasks, MongoDB Atlas helps developers build apps faster with less human error.
About Amazon Kinesis
Amazon Kinesis is a powerful analytics solution that overcomes the batch-processing challenges of Hadoop — and similar solutions — which don’t allow real-time precision in decision-making because they can’t rapidly process high volumes of streaming data. With its ability to process hundreds of terabytes of streaming data per hour, Kinesis allows you to develop apps that rely on real-time data to fuel AI analytics, machine learning insights, and other applications. Kineses enables instant responses by eliminating the delay associated with batch processing.
Popular Use Cases
Bring all your Amazon Kinesis data to Amazon Redshift
Load your Amazon Kinesis data to Google BigQuery
ETL all your Amazon Kinesis data to Snowflake
Move your Amazon Kinesis data to MySQL
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MongoDB Atlas's End Points
MongoDB Atlas Automated Features
The automated security features included in MongoDB Atlas let you monitor who has access to your data while securing your information against unwanted intrusions. Also, due to the platform's automation of mundane operational tasks — like provisioning and configuration, patching and upgrades, monitoring and alerts, advanced security automation, and disaster recovery — you don't have to be a data science expert to set up and run your databases.
MongoDB Advanced Security Automation
MongoDB Atlas provides a variety of database security layers including advanced access control, IP whitelists, in-flight data encryption through TLS/SSL, optional encryption of your file system, and network isolation through Amazon VPCs and VPC Peering.
MongoDB Atlas Built-In Replication
MongoDB Atlas offers multiple servers to provide 'always-on' availability. Even if your primary master goes down, multiple backups ensure that your system is always up and running.
MongoDB Atlas Backups and Time-Machine Recovery
The advanced backup and recovery features for MongoDB Atlas guard against data corruption. Whether the threat is from hackers or a team member's innocent mistake, you can rest easy knowing that, after a catastrophic, event you'll have a backup copy to recover your system from a specific time in the past.
MongoDB Atlas Detailed Statistics and Monitoring
MongoDB Atlas provides detailed information and statistics about your database systems. By organizing this information in numerous ways, the platform helps you understand when important changes or upgrades to your system may be necessary. Moreover, if it's time to make changes, you can provision new server instances in a flash.
MongoDB Atlas Automated Patches and Upgrades
Whether it's a new technology upgrade to improve database efficiency or a security patch to protect against a new security threat, MongoDB Atlas automatically upgrades or lets you upgrade with a single click, so you can take advantage of features as soon as they're available. Upgrades happen in a matter of minutes without any downtime required.
MongoDB Atlas Customizable Database Tools
MongoDB Atlas includes a suite of tools that allow you to select your regions, billing options, and more — allowing you to customize server instances to your desired specifications.
MongoDB Atlas Scalability
MongoDB Atlas scales up and down — or scales out horizontally through automatic sharding — according to the needs of your company with zero application downtime. This allows you to grow beyond the limitations of one server without making your application too complex. Moreover, the platform's automatic balancing keeps information equally distributed across multiple replica sets as your data volumes grow, or as your cluster increases or decreases.
Amazon Kinesis's End Points
Amazon Kinesis Video Streams
Amazon Kineses Video Streams allow you to safely ingest streaming video data from millions of linked devices into AWS for machine learning, analytical, and other processing purposes. The platform then encrypts, stores, and indexes the video data so you can access video with simple APIs, play live video streams, and offer on-demand playback. When incorporating this technology with Amazon Rekognition Video, TensorFlow, ApacheMxNet, and OpenCV, Amazon Kineses Video Streams makes it possible to build video analytics and computer vision processes into your applications.
Amazon Kinesis Data Streams
By capturing, processing, and storing data streams, Amazon Kinesis offers a real-time data streaming solution to ingest large amounts of information from hundreds of thousands — even millions — of sources at the gigabytes-per-second scale. The massive scalability of this solution lets you capture and produce immediate analytics on data pertaining to financial transactions, database event streams, location tracking data, clickstreams, social media activity, and more. Since the availability of streaming data happens in milliseconds, the platform enables real-time analytics of this information for instant detection of anomalies, dynamic price adjustments, precise dashboard metrics, and more.
Amazon Kinesis Data Firehose
Amazon Kinesis Data Firehose provides a simple and durable way to pull your streaming data into data warehouses, data lakes, and analytics solutions. Due to its compatibility with Splunk, Amazon Redshift, Amazon S3, and Amazon Elasticsearch Service, Kinesis Data Firehose empowers real-time data analytics for the dashboarding and BI tools you've come to trust. Fully managed and automatically scaling, you can use Firehose to encrypt, batch, transform, and compress your information before ingestion to boost security and save on disk space.
Amazon Kinesis Data Analytics
Amazon Kinesis Data Analytics helps users without programming knowledge to analyze data streams with SQL or Java. For team members who know SQL, an SQL editor and templates are available for creating streaming applications or querying streaming data. Meanwhile, those with Java knowledge can develop more nuanced streaming applications that perform real-time data transformations and analytical processes.