In today’s time, the growth of any business is directly linked to its capacity to handle data processing at a large scale. As companies started to grow, they started generating huge amounts of data with variable rates and volumes. So it becomes crucial to set up a robust cloud strategy in place.

The Internet of Things has accelerated the growth of data, both structured and unstructured. It is required to extract valuable insights from this data to drive profitable decisions as an ultimate goal. To acquire this processing and decision-making capability, businesses need to generate capacity to gather, store and retrieve data with ease.

Amazon S3

Amazon S3 solves this problem as Amazon Web Services (AWS) provides the most basic and global infrastructure.

Amazon S3 allows high scalability, secured, and low-latency data storage from the cloud. With its user-friendly web service interface, one can easily store and access data on Amazon S3 from anywhere on the web. It starts by selecting a region, creating a bucket, and transporting to quickly and easily share data.

Amazon S3

Also, with Amazon S3, we don’t need to predict future data usage. You can store and access as much data (though individual objects can only be up to 5 terabytes in size) you want and whenever you want to.

Amazon S3 automatically creates multiple copies of your data to keep it secure and restorable in case of any unfortunate event. Furthermore, with almost zero budget and setup cost, life cycle policies such as moving your less-used data to Amazon Glacier to reduce cost and secure it from unauthorized access, Amazon S3 allows you to utilize most of the data efficiently way without any hurdle.

Key Benefits of Amazon S3

Amazon S3 is a trendsetter in cloud data storage and has numerous advantages, but let’s discuss the five most notable ones:

  • Trustworthy and Secure Infrastructure:

When created, Amazon S3 buckets can only be used by the identity that created them (IAM policy grants are an exceptional case). Also, you can manipulate access control for each file and bucket. Thus, IAM (Identity access management) sustains your complete control over how, where, and by whom the data can be accessed. Amid such a set of regulations and authorization policies, you can ensure no unauthorized access to your data.

  • 24/7 Availability:

Amazon S3 provides full-time and full-scale service access to the fast and inexpensive data storage facility that Amazon itself uses to carry out the operations of its global network of websites. S3 Standard is set up for 99.99% availability & Standard – IA is set up for 99.9% availability as both enjoy the patronage of the Amazon S3 Service Level Agreement, which Amazon strictly follows.

  • Low-Maintenance:

Amazon S3 bounds you to only pay for the data you are using, making it a low-maintenance service that costs you around $0.022 / GB to approximately $0.0125 / GB for infrequent access. You can also outline policies for automatic data transition to the infrequent access framework of Amazon Glacier, which further lessens the cost as Amazon Glacier is cheaper(approx $0.004 / GB).

  • Ease of Migration:

Amazon S3 provides you with various migration options such as the S3 command-line interface and Glacier command-line interface. This migration practice is very budget-friendly, and it is very easy to transfer huge amounts of data to Amazon S3 or out of Amazon S3. Amazon S3 also allows you to import or export data to any external device or on any network.

  • Simple and Easy Management:

Amazon S3 possesses a very user-friendly web interface that eliminates the usual headache of technical workload by allowing easy and instant security maintenance, capacity optimization, and data management in the most suitable way possible. You can outline your lifecycle policy, set up replication scenarios, and reshape the Amazon S3 inventory. It also enables you to apply different filters to a better and more managed outlook of your storage.

Also Read: 8 Applications of Data Clustering Algorithms


Snowflake is a data warehouse built on top of the Amazon Web Services (AWS) or MS Azure cloud framework. It lacks any hardware or software to choose, install, or handle as a low-maintenance solution, making it a perfect choice for organizations that don’t want to allocate resources or spend dollars on setup, maintenance, and support on-site servers. And data can be transported easily into Snowflake via an ETL (Extract, transform and load) framework.


But the attributes of Snowflake that make it unique are its architecture and data sharing capabilities. The Snowflake architecture enhances the storage and computation process by allowing both to work independently, so customers can either use and pay for storage and computation separately or for both. And the sharing option is capable enough to allow organizations to quickly and easily share regulated and secured data in real-time.

Also Read: How to Build an Effective AI Model for Business

Key Benefits of Snowflake

Snowflake is designed specifically for the cloud, and it’s developed to fill all voids in older hardware-based frameworks, such as limited scalability, data transformation problems, and delays or crashes due to numerous query requests. 

Here are five perks of integrating a Snowflake framework into your business.

  • Speed and Reliability:

The highly scalable model of the cloud means if you want faster data transfer or make multiple queries at a time, you can escalate your virtual warehouse to enjoy the perks of extra computational power and capacity. Once you are done, you can scale back to the previous capacity without paying continuously for abandoned resources.

  • Capacity and Support for Structured and Semistructured Data:

You are not required to transform, convert or pre-process any data before analysis. Instead, once the data is available, you can directly combine both structured and semi-structured data for analysis and load it into your cloud database as a snowflake is capable enough to optimize how the data is stored and processed.

  • Concurrency and Accessibility:

With conventional data warehouse frameworks, a large number of users, and queries being made, you could encounter concurrency issues (such as delays or crashes) as more users and queries tend to occupy more resources.

Snowflake eliminates the concurrency issues with its state-of-the-art multicluster framework by allowing queries from one virtual warehouse to refrain from impacting the queries. Furthermore, each virtual warehouse can be either scaled up or down as per requirements. As a result, data analysts and data scientists can achieve what they are struggling for, whenever required, without delays due to slow loading or preoccupied resources.

  • Effortless Data Sharing:

Snowflake’s architecture allows data sharing among Snowflake users and helps organizations share data with any data consumers (whether they are snowflake customers or not) without any effort. In addition, this functionality allows the service provider to develop and configure a Snowflake account for multiple consumers.

  • Availability and Security:

Snowflake is designed to operate with consistent and continuous availability. It can bear component and network failures with minimal impact on customers. It possesses SOC 2 Type II certification and additional layers of security, including support for PHI data for HIPAA customers and encryption across all network protocols.

Delta Lake

To understand the role of Delta Lake, it is necessary to understand what Data lakes are. Data Lakes are very puzzled and messy pools of data as everything gets dumped there. Sometimes, there is no need or reason to dump data in data lakes, but we may be thinking we’ll need it for later usage. Much of this mess and lack of organization happens due to the many small files and many data types. 

Because multiple small files are not in a suitable format, reading them in any shape or form is difficult, if not impossible. In addition, data lakes often possess poor data or corrupted files, so it becomes impossible to analyze such files. All you can do to handle such files is to roll back and start over again.

delta lake

This is where Delta Lake appears as a candle in the dark. It provides an open-source storage layer that offers ACID transactions: Atomicity, Consistency, Isolation, Durability, to Apache Spark big data framework. So, instead of the mess discussed above, you get an extra layer of your data lake from Delta Lake. Delta Lake enriches ACID transactions utilizing a log linked with each Delta table created in your data lake. This log possesses the listing of everything that was ever performed on that data table or data set, which provides high levels of reliability and stability.

Key Benefits of Delta Lake

  • ACID Transactions:

With Delta, you don’t need to bother writing any code – the transactions written to the log are automated. This transaction log is the key, and it portrays a single source of data as truth.

  • Scalable Metadata:

Delta Lake can handle terabytes or even petabytes of data without any problem. Metadata is stored just like mainstream data, and you can view it using an option called Describe Detail which will elaborate the detail of all the metadata linked with the table.

  • Single Platform enriching Batch & Streaming:

Delta lake no longer requires a separate framework for reading a data stream against a batch of data, eliminating streaming and batch flux limitations. Instead, you can do parallel streaming or write a batch to your table, and it all gets logged automatically, making your clicks and decisions reserved.

  • Schema Enforcement:

This is what makes Delta Lake unique as it enforces your schemas. If you write a schema on a Delta table and push data to that table that is not consistent with the schema, it will pop up an error and restrict you from writing that, holding you back from making wrong inputs. The enforcement mechanism reads the schema as a metadata segment by analyzing every column, data type, etc. It checks whether or not your input to the Delta table is the same as what the schema represents for that Delta table. Thus, it frees your mind from the tension of writing wrong data to the wrong table.

  • History Preservation:

You can retrieve an older version of your data using queries to make rollback or audit data decisions.

  • Merge, Insert and Delete:

Delta enables you to carry out operations like upsert easily or merge. Merges works like simple SQL merges into your Delta table. You can perform merges over data from another data frame into your Delta table and perform update operations like insertion and deletion. You can also perform a regular insertion or deletion of data using a predicate on a table. This regular update option is something that is not offered by any platform except Delta.

  • Compatibility with Apache Spark

Apache Spark is the top-of-the-line processing framework for big data. Delta Lake adds value to Spark by ensuring reliability. Doing so enables analytics and machine learning initiatives to gain ready access to high-quality and reliable data.

Comparison Between Snowflake vs. Amazon S3 vs. Delta Lake

Factors Snowflake Amazon S3

Delta Lake

Continuous Data Integration Has built-in options such as STREAMS It is attained using different technology or tools such as AWS Glue, Athena, and Spark. It can be attained using the ETL framework.
Consuming / Exposing Data. Snowflake has JDBC, ODBC, .NET, and Go Snowflake Drivers. Additionally, it has Node.js, Python, Spark, and Kafka Connectors. Snowflake also provides Java & Python APIs to simplify working in REST API. REST API, SOAP API(Depreciated), JDBC & ODBC Drivers. Connectors for JS, Python, PHP, .NET, Ruby, Java, C++ and for NodeJS. Delta ACID API for consuming and delta JDBC connector for exposing.
SQL Interface Built-in (Worksheets) Need Athena/Presto (additional cost) Apache Spark SQL, Azure SQL, Data Warehouse/DB
Sharing of Data Across Accounts Actual data is not copied or shared with another account. For example, no Editor rights can be provided to a consumer account. It is achieved using a simple “share” command, which pushes computational cost but no storage cost. Accessing files across accounts can be achieved using Amazon Quick Sight, which incurs additional costs. Sharing of data is made possible under Azure Data Share, which is based on snapshot-based sharing. Azure Data Share pushes a cost for the operation to move a dataset from source to destination, including the cost for the resources engaged in moving the data.
Compression (Data Storage) Automatically compresses the file as it stores data in a tabular format. It can be achieved manually using EC2 machines. Loads all data in Apache Parquet file format to experience efficient compression.
Native Stack (better integration) The Snowflake partner tools provide a better integration than other tools Amazon Stack (Amazon S3 – Storage, Amazon Redshift – Datawarehouse, Amazon Athena – Querying, Amazon RDS – Database, AWS Data Pipeline – Orchestration, etc.) Microsoft stack (BLOB – Storage, Azure Databricks – Data Preparation, Azure Synapse Analytics – Data Warehouse, Azure SQL DB – Database, Azure DevOps, Power BI – Reporting, etc.)
Supported Formats Structured & semi-Structured Data (JSON, AVRO, ORC, PARQUET, and XML.) Structured, semi-structured & Unstructured Data Structured, semi-structured & unstructured data.
Data with updates Updates the specific rows in the table with new values where the condition matches. We cannot insert data or delete or modify just a segment of an existing S3 object. We are bound to read the object, make changes to the object, and then write the whole object back to S3. We cannot update data in S3. We can only read and rewrite the entire object back to S3. It allows us to update specific values in the data based on the condition.


This blog post must have helped you choose between Snowflake vs. Amazon S3 vs. Delta Lake frameworks as per your feasibility. Delta Lake and Snowflake are much better choices for handling data that lacks organization and structure. Moreover, all these solutions are highly scalable, allowing you to enjoy the perks of these services without spending too much on unnecessary space and processing.