MDB Analytics

Question 1

A fully managed storage solution that is optimized for complex analytics over large data sets while delivering the low cost economics of cloud storage

Answer 1

Data Lake

Question 2

A centralized repository designed to store, process, and secure large amounts of structured, semistructured, and unstructured data

Answer 2

Data Lake

Question 3

A distributed query engine that allows you to natively query, transform, and move data across various sources inside & outside of MongoDB Atlas

Answer 3

Atlas Data Federation

Question 4

Query and aggregate data across multiple Atlas clusters, Atlas data lakes and AWS S3 buckets to get a holistic view of your data

Answer 4

Atlas Data Federation

Question 5

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

Answer 5

Atlas Data Federation

Question 6

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

Answer 6

Atlas SQL Interface

Question 7

Analysts can build complex aggregation pipelines to surface insights and create new data streams without time-consuming data manipulation

Answer 7

Atlas SQL Interface

Question 8

Enables tools such as Tableau, Looker, and Power BI to access and visualize data directly from MongoDB Atlas

Answer 8

Atlas SQL Interface

Question 9

All database changes are published to an API, notifying subscribing applications when an event matches a predefined criteria

Answer 9

Change Streams

Question 10

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

Answer 10

Change Streams

Question 11

Automatically execute application code in response to the event, allowing you to build reactive, real time in-app analytics

Answer 11

Atlas triggers and functions (App Services)

Question 12

Automatically run code in response to database changes, user events, or on preset intervals

Answer 12

Atlas Triggers

Question 13

Define and execute JavaScript functions to build APIs, integrate with cloud services, and more

Answer 13

Atlas Functions

Question 14

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

Answer 14

Atlas Online Archive

Question 15

Examples: IoT sensor data, financial trades, clickstreams, and logs are all valuable sources of insights and analytics for every business.

Answer 15

Time series data

Question 16

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.

Answer 16

Column Store Indexes

Question 17

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.

Answer 17

MDB Connector for Apache Kafka

Question 18

Allows Spark jobs to read from and write to MongoDB as part of your data science and data engineering platform

Answer 18

MDB Connector for Apache Spark

Question 19

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

Answer 19

Cluster to Cluster Sync

Question 20

Physically isolated from nodes supporting the operational workload

Answer 20

Atlas Analytical Nodes

Question 21

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

Answer 21

MDB Query API/Engine