Introduction
In the era of big data, cloud platforms have become
essential for various data engineering tasks such as data storage, data
ingestion, integration, processing, and analytics. Data and analytics workload
types are diversified in nature, careful consideration is required to select
the right storage types, which integrates well with various data types,
ingestion, crunching, and consumption patterns. This is pivotal for building a
robust data and analytics platform. Major cloud providers—Microsoft Azure, Amazon
Web Services (AWS), and Google Cloud Platform (GCP) —provide robust data
storage services each having unique strengths and ideal use cases.
Understanding their differences can help organizations select the best storage
for their data platform needs.
In this article, we compare the storage offerings from AWS,
Azure, and GCP, focusing on their strengths, and recommendations for different
scenarios.
Data Storage
Data storage solutions are diverse, each designed to meet
specific requirements of data types, access patterns, scalability, and
performance. Below is a breakdown of different types of data storage— Object
Storage, SQL, NoSQL, MPP with Columnar Storage — and their typical
applications.
Object Storage
Object storage stores data as objects in a flat structure
with metadata, making it highly scalable and ideal for unstructured data.
Each object typically contains the data itself, metadata, and a unique
identifier. Object storage is accessed through APIs (such as REST) rather than
SQL. However ephemeral cloud big data compute engines (e.g. spark on
Databricks, Snowflake) integrated with a Meta Store (e.g. Hive Meta Store) can
facilitate querying the data using SQL syntax. Utilizes schema on read
pattern.
Strengths
Massive Scalability: Designed to store large
quantities of unstructured data across distributed environments.
Data Durability: Ensures data redundancy, making it
suitable for high-durability storage.
Applications in Data & Analytics
Data Lakes: Used for storing raw data in data lakes,
supporting big data analytics.
IoT and Log Data Storage: Stores vast amounts of log
and sensor data from IoT devices in a cost-effective way.
Archival: Cost-effective storage options for
infrequently accessed data.
Offerings from Cloud Providers
Amazon AWS: S3
Microsoft Azure: ADLS Gen2, Azure BLOB Storage
GCP: Google Cloud Storage
Typical Deployment Pattern:
Multi-Cloud Data Lake
SQL Store (Relational Datastores)
SQL databases, also known as relational databases,
store data in a structured format with predefined schemas (i.e. schema on
write pattern) in tables consisting of rows and columns. Data is accessed
using SQL (Structured Query Language), and relationships between tables can be
defined and managed using keys and constraints. In a typical data and analytics
scenario, the relational databases are deployed as DataMart towards the
consumption (serving) layer, typically for low latency access requirements.
However, with In-Memory BI & Reporting tools (e.g. Power BI, Qlik, Tableau)
complemented with cache-enabled Lakehouse platforms (e.g. Snowflake), columnar
MPP Data Warehouse (e.g. Azure Synapse dedicated SQL pool ) and vectorized
query engine (Databricks photon), this deployment pattern is getting obsolete
slowly.
Strengths:
ACID Compliance: Ensures reliable transactions
with atomicity, consistency, isolation, and durability.
Data Integrity: Strict schema requirements enforce
data consistency and integrity.
Complex Querying: Powerful SQL language supports
complex queries, joins, and aggregations.
Applications in Data & Analytics:
Data Warehousing (in OLTP-focused scenarios): Smaller
to medium-scale data warehouses / DataMart can utilize relational databases for
reliable low latency data access.
Offerings from Cloud Providers
Amazon AWS: AWS RDS (Managed Service)(MariaDB,
Oracle, MySQL, MS SQL, PostgreSQL), AWS Aurora (Serverless)(PostgreSQL, MySQL).
Microsoft Azure: Azure Database (Managed
Service)(MySQL, MS SQL, PostgreSQL), Azure SQL (Serverless).
GCP: Google Cloud SQL (Managed Service)(MySQL, MS
SQL, PostgreSQL), Cloud Spanner (Serverless) horizontally scalable
Typical OLAP Deployment Pattern:
SQL Store in Data & Analytics Platform
NoSQL Databases (Non-Relational Databases)
NoSQL databases provide a flexible approach to data storage without
requiring fixed schemas. These databases can handle unstructured,
semi-structured, or structured data and come in several types, including document
stores, key-value stores, column-family stores, and graph databases.
Types of NoSQL Databases:
Document Stores: Store data in documents (e.g., JSON,
BSON), where each document can have a different structure.
Examples: MongoDB, Couchbase
Key-Value Stores: Use key-value pairs for quick
lookup and are highly scalable.
Examples: Redis, Amazon DynamoDB
Column-Family Stores: Organize data in column
families, suitable for sparse data and fast access.
Examples: Apache Cassandra, HBase
Graph Databases: Model data as nodes and edges,
useful for complex relationship mapping.
Examples: Neo4j, Amazon Neptune
Strengths:
Flexible Schema: Adapts to changing data models
without rigid schema requirements.
High Scalability: Can handle large volumes of data
and horizontal scaling (especially key-value and document stores).
Optimized for Specific Access Patterns: Each type
caters to a unique use case, e.g., graph databases for relationship-based data.
Applications in Data & Analytics:
Social Media Analytics and User Profiles (Guest,
Customer): Document stores and graph databases are used to store dynamic
data structures (e.g., user profiles, social connections).
Real-Time Analytics: Key-value stores, like Redis,
are used for caching and real-time analytics in gaming, ad tech, and IoT.
Recommendation Engines: Document and column-family
stores are ideal for handling semi-structured product catalog data and
recommendation algorithms.
Offerings from Cloud Providers
Amazon AWS: AWS DynamoDB, AWS Document DB, AWS
Keyspaces, AWS Neptune
Microsoft Azure: Azure Cosmos DB, Azure Table
GCP: Google Big Table, Cloud Fire Store
Typical Deployment Pattern:
No-SQL Stores in Data & Analytics Platform
MPP with Columnar Storage
It is a database architecture designed for handling
large-scale data analytics by distributing query processing across multiple
processors or nodes in parallel. MPP databases leverage columnar storage,
where data is stored column by column rather than row by row, enabling highly
efficient read operations for analytical workloads. This combination optimizes
performance for complex queries, such as those involving aggregations,
filtering, and joins, as it minimizes I/O by accessing only the required
columns instead of entire rows. Examples of MPP databases with columnar storage
include Amazon Redshift, Azure Synapse Analytics, Snowflake, and Google
BigQuery.
Strengths:
High Scalability: Workloads can be distributed
across numerous nodes.
Efficient query execution: Parallel query processing
Data Compression: Columnar storage reduces redundancy
within each column.
Optimized Analytics: Enabling faster performance for
data warehousing and business intelligence tasks.
Deployment Pattern
Azure Synapse Analytics in Data Platform
Conclusion
Selecting the right storage ultimately depends on the use cases, data types, latency requirement, scalability and long term data-analytics strategy of an organization. Each storage offers distinct benefits, and, in some cases, a hybrid or multi-cloud approach may be ideal.
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