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Sergey Nivens - stock.adobe.com

Sergey Nivens - stock.adobe.com

Feature


5 CLOUD DATABASE COMPARISON TIPS TO GUIDE YOUR DATA STRATEGY




CATCH UP ON THESE TIPS THAT COMPARE THE STRENGTHS, WEAKNESS AND AVAILABLE
INTEGRATIONS OF POPULAR PUBLIC CLOUD DATABASE AND ANALYTICS SERVICES FROM AWS,
MICROSOFT AND GOOGLE.

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By
 * Sarah Neenan, Assistant Site Editor

Published: 23 Jan 2020

Organizations host vast amounts of data in the cloud, compiled in a variety of
data types and structures. The database and analytics services that handle this
data are central pieces of an organization's cloud strategy, which is why
enterprises must ensure their chosen services meets their structure,
availability and security requirements.



Google, Microsoft and AWS offer an extensive range of database and analytics
services tailored to different needs. But with the overwhelming amount of
choices, it can be a challenge to know which database or analytics service fits
your specific requirements.

Use these five tips to help choose the right one. Whether you want to boost your
analytics capability or need a service to handle unstructured data, these cloud
database comparisons will ease the selection process.


COMPARE GOOGLE FIREBASE REALTIME DATABASE OR GOOGLE CLOUD FIRESTORE

Google Firebase Realtime Database and Google Cloud Firestore are two of the
NoSQL database options available on Google Cloud Platform. Both support mobile
and web application development, but differ in terms of data structure, offline
support, security and querying capabilities. Firebase Realtime Database, the
original NoSQL database on the Firebase platform, can handle simple data, while
Cloud Firestore is more suitable for complex data structures and querying needs.
Take a closer look at Google Cloud Firestore vs. Firebase Realtime Database to
determine which database meets your application and data requirements.


BREAK DOWN AZURE COSMOS DB USES AND CAPABILITIES

Organizations often have many different kinds of data to store and process,
which means they'll need more than one type of database. Azure Cosmos DB, a
multimodel database fabric, offers support for various database types, such as
graph, document and relational. Cosmos DB provides the benefits of scale-out
architecture without common consistency issues. Explore the core capabilities
and uses for Azure's multimodel database and how it stacks up to NoSQL databases
from other cloud providers, particularly Amazon DynamoDB.

The timeline of Azure Cosmos DB.



MACHINE LEARNING AND SERVERLESS UPDATES IMPROVE BIGQUERY, BUT WORK REMAINS

BigQuery, Google's big data analytics service, processes large read-only data
sets. Recent updates to BigQuery have focused on machine learning and real-time
analytics, as well as serverless capabilities. Compared to querying services
from other cloud providers, such as Amazon Redshift and Azure SQL Data
Warehouse, Google BigQuery provides faster queries for large data sets, but it
has some catching up to do in areas such as regional availability, data imports
and numeric support. See where BigQuery stands among its competitors with this
overview on notable updates and remaining shortcomings.


DOCUMENTDB VS. DYNAMODB: COMPARE AWS NOSQL DATABASES

AWS users have two choices when it comes to native document databases. DynamoDB
is a managed key-value store that supports document data, and DocumentDB is a
managed service with MongoDB compatibility. These databases fall within the same
category but differ in important ways. For example, DynamoDB is better for
applications that access data on unique keys, while DocumentDB offers more
flexible indexing options. Evaluate DocumentDB vs. DynamoDB with this AWS cloud
database comparison.


AWS DATA LAKE VS. DATA WAREHOUSE OPTIONS FOR THE CLOUD

Data lakes and data warehouses help organizations ingest and analyze large sets
of data in the cloud, and AWS provides native services for both options.
Developers can build data lakes with AWS Lake Formation or build a data
warehouse with Amazon Redshift. In this cloud database comparison tip, learn the
differences in features and use cases for data lakes and data warehouses on AWS,
and identify ways these technologies can work together for storage and
analytics.




DIG DEEPER ON CLOUD DEPLOYMENT AND ARCHITECTURE

 * AWS CLOUD DATABASE USERS DETAIL APPLICATIONS
   
   
   By: Sean Kerner

 * A CLOUD SERVICES CHEAT SHEET FOR AWS, AZURE AND GOOGLE CLOUD
   
   
   By: Trevor Jones

 * A BETTER WAY TO QUERY DYNAMODB DATA WITH SQL
   
   
   By: Joe Emison

 * GOOGLE ENHANCES CLOUD SPANNER AND OTHER DATABASES
   
   
   By: Sean Kerner

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