MDB Analytics Flashcards
A fully managed storage solution that is optimized for complex analytics over large data sets while delivering the low cost economics of cloud storage
Data Lake
A centralized repository designed to store, process, and secure large amounts of structured, semistructured, and unstructured data
Data Lake
A distributed query engine that allows you to natively query, transform, and move data across various sources inside & outside of MongoDB Atlas
Atlas Data Federation
Query and aggregate data across multiple Atlas clusters, Atlas data lakes and AWS S3 buckets to get a holistic view of your data
Atlas Data Federation
With __________, you can access all of these different data sources in a single query to build your forecasting model, all without the need to first move or transform data, or change the query as data moves between sources
Atlas Data Federation
Enables analysts to leverage their existing SQL skills and tools to query data in MongoDB Atlas. This avoids them having to learn the more developer-centric MongoDB Query API
Atlas SQL Interface
Analysts can build complex aggregation pipelines to surface insights and create new data streams without time-consuming data manipulation
Atlas SQL Interface
Enables tools such as Tableau, Looker, and Power BI to access and visualize data directly from MongoDB Atlas
Atlas SQL Interface
All database changes are published to an API, notifying subscribing applications when an event matches a predefined criteria
Change Streams
Applications can use _______ to subscribe to all data changes on a single collection, a database, or an entire deployment, and immediately react to them. Because ________ use the aggregation framework, applications can also filter for specific changes or transform the notifications at will
Change Streams
Automatically execute application code in response to the event, allowing you to build reactive, real time in-app analytics
Atlas triggers and functions (App Services)
Automatically run code in response to database changes, user events, or on preset intervals
Atlas Triggers
Define and execute JavaScript functions to build APIs, integrate with cloud services, and more
Atlas Functions
Aged data can automatically be tiered out of hot time series collections to low cost object storage, while preserving the ability to query it any time
Atlas Online Archive
Examples: IoT sensor data, financial trades, clickstreams, and logs are all valuable sources of insights and analytics for every business.
Time series data
Speeds up ad-hoc analytics queries that aggregate specific fields across most or all documents in a collection. Examples include computing counts, averages, and min/max values, i.e. maintaining a running sales total and average sales price over the duration of a product promotion.
Column Store Indexes
configures MongoDB as both a source and sink within your data pipelines – whether for building reactive, event-driven microservices or for streaming data from MongoDB to centralized analytics systems downstream from your applications.
MDB Connector for Apache Kafka
Allows Spark jobs to read from and write to MongoDB as part of your data science and data engineering platform
MDB Connector for Apache Spark
Provides even higher levels of isolation by continuously replicating live operational data from the transactional Atlas database cluster to an entirely separate cluster dedicated to analytics processing
Cluster to Cluster Sync
Physically isolated from nodes supporting the operational workload
Atlas Analytical Nodes
You can run simple point queries for lightning fast lookups through to building modular, multi-stage aggregation pipelines to run powerful real-time analytics over your data. You can quickly filter, group, join, search and sort data, surface recommendations, and calculate moving averages and cumulative sums over rolling time windows
MDB Query API/Engine
