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Analytics of Big data is being embraced by businesses across the board. They're looking for unknown correlations, hidden patterns, customer experiences, market trends, and other important business data in massive databases. By means of effective marketing, additional income opportunities, and improved customer service, these analytical discoveries are assisting firms in gaining a competitive advantage over competitors.

Two of the most popular frameworks of Big data are Snowflake and Hadoop. If you're looking for analytics of big data platforms, Snowflake and Hadoop is almost probably on your shortlist, or you may already be using one. We'll evaluate these two frameworks of Big data depending on a variety of criteria in this post. However, before you go into the Snowflake vs Hadoop debate, it's vital to understand the basics of both systems.

Snowflake vs Hadoop - Table of Contents

What is Snowflake?

Snowflake Data Cloud is built on a cutting-edge data platform that is offered as Software-as-a-Service (SaaS). Snowflake provides processing, data storage, and analytical solutions that are faster and easier to use as well as being more versatile than conventional systems.

Snowflake is not built on any existing "big data" or database technology software platforms such as Hadoop. Snowflake, on the other hand, integrates a totally new SQL query engine with an ingenious cloud-native architecture. Snowflake gives every capability of a business analytical database to the user, as well as a number of additional special capabilities and features.

Data Platform as a Cloud Service

Snowflake is a true software-as-a-service (SaaS) offering. To be more clear:

  • There is no hardware available to choose, install, manage, or configure (physical or virtual).
  • There isn't much to configure, install or administer in terms of software.
  • Snowflake is responsible for ongoing management, maintenance, tweaking, and updates.


Snowflake's infrastructure is fully cloud-based. Except for optional command-line drivers, clients, and connectors, all components of Snowflake's service run on public cloud infrastructures. Virtual compute instances are used to meet Snowflake's computing needs, and data is kept indefinitely via a storage service. Snowflake does not work with private cloud environments (on-premises or hosted).

Snowflake is not a ready-to-use software package that can be installed by a user. Snowflake is in charge of all software installation and upgrades.

How Does Snowflake Computing Work?

Within the storage layer, Snowflake optimizes and stores data in a columnar manner, arranged into databases as specified by the user. dynamically as resource requirements shift. When virtual warehouses run queries, the cache data from the database storage layer invisibly and automatically.

Check out Top 5 Snowflake Training Courses For 2023

What is Apache Hadoop?

Apache Hadoop is a free and open-source platform for processing and storing huge datasets ranging in size from gigabytes to petabytes. Hadoop allows clustering several computers to analyze big datasets in parallel, rather than requiring a single large computer to store and analyze the data.

Apache Hadoop is made up of four major modules:

  1. HDFS (Hadoop Distributed File System) is a distributed file system that runs on low-end or basic hardware. HDFS outperforms conventional file management systems in terms of data performance, fault tolerance, and native support for huge datasets.
  2. YARN (Yet Another Resource Negotiator) is a tool for monitoring and managing cluster nodes and resource utilization. It keeps track of jobs and tasks.
  3. MapReduce is a framework that aids programmes with parallel data processing. The map task creates a dataset from input data that can be computed using key-value pairs. Reduce tasks utilize the output of the map task in order to aggregate it asked to produce the required result.
  4. Hadoop Common provides a set of shared Java libraries that may be used by all Hadoop modules.


How Does Hadoop Work?

Hadoop makes it simple to use all of a cluster's server's processing and storage capability, as well as to run distributed operations on massive volumes of data. Hadoop serves as a foundation for the creation of additional services and applications.

Apps that capture data in numerous forms can connect to the NameNode via an API call and place data into the Hadoop cluster. The NameNode keeps track of the file directory structure and "chunks" placement for every file, which is repeated among DataNodes. To execute a task to query the data, provide a MapReduce job made up of many maps and reduce processes that operate on the data in HDFS dispersed among the DataNodes. Reducers run on each node to collect and organize the final output, while map tasks are performed on each node against the specified input files.

Because of its extensibility, the Hadoop ecosystem has evolved tremendously over time. The Hadoop ecosystem now comprises a variety of tools and applications for collecting, storing, processing, analyzing, and managing large amounts of data. The following are some of the most popular applications:

  • Spark: It is an open-source distributed processing engine for big data applications that are widely utilized. Apache Spark is a batch processing framework, machine learning, streaming analytics, ad hoc queries, and graph databases, and it supports in-memory caching and optimized execution for rapid performance.
  • Presto: It is a distributed SQL query engine that is optimized for low-latency, ad-hoc data analysis. Complex query joins, aggregations and window functions are all supported by the ANSI SQL standard. Presto can process data from a variety of sources, including Amazon Simple Storage Service (S3) and Hadoop Distributed File System (HDFS).
  • Hive: It provides a SQL interface for leveraging Hadoop MapReduce, allowing for massive-scale analytics as well as fault-tolerant and distributed data warehousing.
  • HBase: It is a non-relational, versioned open-source database that works on Amazon S3 (through EMRFS) or the Hadoop Distributed File System (HDFS). HBase is a distributed and massively scalable big data store designed for real-time access to billions of rows and millions of columns in tables in a random, rigorously consistent manner.
  • Zeppelin: It is an interactive notebook that allows you to explore data in real-time.


Learn The Complete Apache Spark Tutorial

Snowflake vs Hadoop Comparison

Now that you have a working knowledge of both systems. Snowflake and Hadoop can be compared in a variety of ways to determine their capabilities. We'll compare them using the criteria below.

Snowflake vs Hadoop: Which is Faster?

Hadoop was designed with the goal of gathering data continually from a variety of sources, regardless of the data type and archiving it in a distributed system. This is an area in which it shines. MapReduce handles batch processing in Hadoop, while Apache Spark handles stream processing.

Snowflake's virtual warehouses are the most appealing feature. This creates a burden and capacity that is segregated (Virtual warehouse ). This permits you to distinguish or categorize query processing and workloads based on your needs.

Snowflake vs Hadoop: Which is Easier to Use?

You may simply input data into Hadoop using [shell] or by connecting it with a variety of technologies such as Flume and Sqoop. The expense of implementation, maintenance, and configuration are likely Hadoop's worst flaw. Hadoop is difficult, and its appropriate and concurrent use necessitates highly competent data scientists with a working knowledge of Linux systems.

Snowflake, on the other hand, can be set up and running in a matter of minutes. Snowflake does not require any software or hardware installation or configuration. Using the native solutions given by Snowflake, it is also simple to manage/handle many semi-structured data types such as JSON, ORC, Avro, XML, and Parquet.

Snowflake is also a database that does not require any maintenance. It's completely supervised by the Snowflake team, so you don't have to worry about Hadoop clusters maintenance tasks like patchworks and frequent updates.

Related Can Python Connect to Snowflake?

Snowflake vs Hadoop: Cost

Hadoop was considered to be inexpensive, however, it is actually quite costly. Despite the fact that it is an Apache open-source project with no licensing fees, it is nonetheless expensive to deploy, maintain and configure. You'll also have to pay a high total cost of ownership (TCO) for the hardware. Hadoop's storage processing is done on hard disks and therefore necessitates a lot of disc space and computer power.

There is no need to configure/install any software or deploy any hardware in Snowflake. Despite the fact that it is more expensive to use, it is easier to deploy and maintain than Hadoop. Snowflake is used to pay for the following.:

  • The available storage space has been used.
  • The time it takes to do a query on data.

To save money, you can put Snowflake's virtual data warehouses on "pause" while you're not using them. As a result, Snowflake's estimated price per query is significantly lower than Hadoop's.

Hadoop vs Snowflake: Data Processing

Hadoop is a method for batch processing massive static datasets which can be Archived datasets that have been collected over time. Hadoop, on the other side, cannot be utilized to conduct interactive processes or perform analytics. This is due to batch processing's inability to respond swiftly to changing enterprise needs in real-time.

Snowflake features excellent stream and batch processing capabilities, allowing it to serve as both a data warehouse and a data lake. Using a concept known as virtual warehouses, Low latency queries are well supported by Snowflake which many Business Intelligence (BI) users want.

Compute and Storage resources are segregated in virtual warehouses. According to demand, you can scale down or up on computation or storage. Because the computer power scales along with the query size queries no longer have a size restriction, allowing you to retrieve data considerably faster. Snowflake also comes with support for the most popular data formats built-in, which you can query using an SQL server.

Fault Tolerance in Snowflake and Hadoop

Both Snowflake and Hadoop provide fault tolerance, despite their techniques being different. Hadoop's HDFS is strong and dependable, and I've had very few issues with it in my time with it.

It uses distributed architecture and horizontal scaling to deliver high redundancy and scalability. Multi-data center resiliency and Fault tolerance are also integrated into Snowflake.

Snowflake Use Cases

Here are three real-world implementation examples that our organization has assisted clients with to get you thinking about Snowflake's possibilities for your company.

1. Analysis of Retail Transactions

Transaction data come in vast numbers in a retail context. However, data analysis isn't only about numbers. Data must be kept up-to-date and fresh. Even if your processes are well-designed, your ETL and refresh may be limited in terms of time and resources.

Even if you raise the power and size of your servers to remedy your data volume problem, you may need to do so again in the future. Even with normal and frequent database backups, data backups are a problem that may cause you to be concerned. When you add a growing number of present and future data customers to the mix, your retail transaction warehouse's troubles multiply.

One of our clients had a similar experience. The issues made it impossible for them to consume the data and to provide information to many clients who needed to:

  • Analyze retail sales
  • Recognize the effects of the seasons
  • Examine incentive programmes
  • Calculate rebate schemes and report on them.


Snowflake was part of the answer we presented to them. The platform includes a number of features that can aid with these issues, including:

  • Abstraction: With Snowflake Warehouses, you can scale computational power to suit your business's needs without having to change your infrastructure.
  • Role-based Access: Snowflake's data can be accessed by role, and users can use Secured Views to disguise PII info or limit the fields that are available. This implies that teams can view the information they need to do their tasks while still adhering to governance guidelines.
  • Backups: If something goes wrong, Snowflake's Time Travel function saves 90-day backups that are stored on a regular basis, allowing you to rapidly revert to a previous version of the data set (or even "Undrop" a table).


2. Health Care Analytics

By identifying illnesses, behaviours, and environmental factors, trend research can assist healthcare organizations in improving patient outcomes. To do this research, organizations will require vast amounts of public health data.

To make matters more complicated, most businesses don't rely only on their own data. They collaborate with other organizations and make use of their data, which is available in a variety of formats. Flat files, CSV, JSON, and even XML are examples of these formats. It can't be processed uniformly in its current format and reformatting the data would be a big task.

Because public health data is so valuable, other organizations may benefit from their perspectives and research based on the same data. Those various audiences may not be tech-savvy, thus a simple interface is required so that they, too, can examine data for healthcare breakthroughs and research.

Snowflake provides a number of features that can help with these issues. These characteristics include:

  • Reads from an Amazon S3 datalake: Snowflake can read from an Amazon S3 datalake. Staging the heterogeneous data will help arrange it and allow you to see it in a structured or semi-structured manner using the External Tables feature.
  • Snowflake allows you to create semi-formated tables and import all JSON and XML data into your database, object by object, using the Variant columns feature. This allows you to construct structured and user-friendly views.
  • Snowflake's incredibly strong stored procedures enable you to transform all of the different data from many sources into a single format.
  • Snowflake allows you to connect easily to external tools like Tableau using the JDBC adaptor, allowing citizen analysts to access and exploit the data.


3. Machine Learning in Fueling

It would be fantastic to have a crystal ball that could precisely foresee stock market developments. While we all know that a crystal ball isn't possible, you might be able to devise an intelligent solution that will help you lower the danger of making the wrong decision and increase your chances of being correct.

You'd use historical stock and trading data, as well as news and legislation data, to create this type of solution. Everything that affects the analysis of your machine learning application should assist you to create more accurate predictions. Unfortunately, some of this data will have to be manually collected and curated, resulting in a solution that isn't entirely automated.

To increase performance, the model will need to be regularly retrained as the dataset develops, and it will need to be extremely responsive so that system users have enough time to act on the data it generates.

Naturally, as you add more data to your dataset and improve your performance, more people will want to see and use the solution's outputs.

Snowflake has a number of capabilities that can help with this hypothetical application's requirements:

  • Data that has been manually uploaded: SnowSQL allows you to upload curated data straight into tables.
  • User-defined functions: The Snowpark feature allows you to create user-defined functions (UDFs) in Scala (Python and Java support is coming soon) that can run natively and be used with stored procedures to offload a lot of processing from your servers.
  • Multi-cluster warehouse: Assigning a big, multi-cluster warehouse to your team in Snowflake allows you to perform several high-volume queries at the same time with fast responses.
  • Monetization: You may monetize your huge and valuable dataset by selling it on the Snowflake data marketplace.


Apache Hadoop Use Cases

Understanding Hadoop use cases can help you better understand what it is and how it works in the real world. It also assists you in determining which Hadoop architecture is best for your company so that you don't make a mistake in tool selection and reduce the system's efficiency. Here are five examples of real-world applications.

1. Finance and Hadoop

Finance and IT are the most common consumers of Apache Hadoop since it aids banks in customer evaluation and marketing for legal systems. A cluster is used by banks to develop risk models for customer portfolios.

It also assists banks in keeping a better risk record, which includes information on saving transactions and mortgages. It can also be used to assess the global economy and generate value for clients.

2. Healthcare and Hadoop

Another big Hadoop user is the healthcare industry. By tracking large-scale health indexes, it aids in the cure of diseases, the prediction of epidemics, and the management of epidemics. Hadoop is primarily used in healthcare to maintain track of patient records.

It supports unstructured healthcare data that can be processed in parallel. Users can process gigabytes of data with MapReduce.

3. Hadoop in the Telecom Industry

Mobile firms have billions of clients, and old frameworks made it tough to keep track of them all. Take, for example, Nokia, a telecommunications business.

The corporation has a lot of data from their made and produced phones. All of this information is semi-structured and must be properly kept and assessed. Nokia can handle and analyze petabytes of data with HDFS, making it easier to consume.

In the telecom business, it is mostly utilized for call data records administration, telecom data equipment servicing, infrastructure planning, network traffic monitoring, and the development of new goods and services.

4. Hadoop in the Retail Industry

Data management software is required by any large-scale retail organization with transactional data. Data is gathered from a variety of sources and utilized to forecast demand, create marketing campaigns, and boost profits.

So, how is Hadoop employed in the retail industry? MapReduce can use historical data to forecast sales and boost profits. It examines a previous transaction before adding it to the cluster. This information can then be utilized to create apps capable of analyzing large volumes of data.

What is the Main Difference Between Snowflake and Hadoop?

Hadoop's storage processing is disk-based and therefore necessitates a lot of disk space and computing power. There is no need to deploy any hardware or configure/install any software in Snowflake. Although it is more expensive to use, it is easier to deploy and maintain than Hadoop.

Snowflake or Hadoop: Who Wins?

You will consider utilizing one at some point in the future since you know the advantages of cloud data warehousing. While Hadoop has aided Big Data innovation, it has also earned recognition for being difficult to set up, provision, and operate. Furthermore, a typical Hadoop data lake could not deliver the capability of a data warehouse natively. Hadoop, for example, includes:

  • SQL DML semantics such as UPDATE, INSERT and DELETE commands do not have native support.
  • There is no POSIX conformance.
  • Using relational data adds to the complexity.

Check out Snowflake Migration Best Practices


When tried compared to Hadoop, Snowflake allows clients to extract more information from enormous datasets, produce significant value, and ignore lower-level activities if their competitive advantage is delivering goods, services, or solutions. Even if you wish to keep adding your data into Snowflake or another data warehouse, when it comes to entirely managed ETL, there is no better solution than Snowflake.

It is, in reality, a No-code Data Pipeline that will assist you in transmitting data from multiple sources to the desired destination. It's constant and dependable. There are pre-built implementations from over 100 different sources.

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Liam Plunkett

Solution Architect

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