Snowflake vs Hadoop

Organizations from various industries are planning to invest in big data analytics. They are trying to analyse huge databases to find hidden patterns, undiscovered correlation coefficient, industry trends, customer engagement, as well as other beneficial company information. These statistical analyses are assisting institutions in creating a competitive edge over competitors through more successful advertising, additional revenue possibilities, and improved operations. Snowflake and Hadoop are two well-known Big Data frameworks. If you are looking for a big data analytics platform, Hadoop and Snowflake are almost certainly on your list, or you may already be using one of these systems. In this post, we will compare these two Big Data frameworks using various parameters. However, before delving into the Snowflake vs. Hadoop debate, it is critical to understand the following: In this blog post we are going to learn about the snowflake, hadoop, key differences between snowflake and hadoop and benefits as well.

Snowflake Vs Hadoop - Table of Content

What is a snowflake?

Snowflake is an advanced cloud data warehouse which creates a consistent integrated solution that allows storage, compute, and workgroup resources to scale up, out, or down to any level required at any time.


Why snowflakes?

Snowflake eliminates the need to pre-plan or size compute demands months in advance. You simply need to add more compute power, either automatically or by pressing a button.

Snowflake could indeed natively consume, store, and request a wide range of structured and semi-structured data, including CSV, XML, JSON, AVRO, and others. This data can be queried in a fully relational manner using ANSI, ACID-compliant SQL. Snowflake accomplishes this with no data pre-processing and no need to perform complicated transformations.

This implies you can confidently solidify a data warehouse as well as a data lake into a single system to support your SLAs. Snowflake offers the ability to load data in parallel without interfering with existing queries, as well as the flexibility to easily expand as your data and data processing needs grow.

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What is Hadoop?

Hadoop is a Java-based structure for storing and processing large amounts of data across computer clusters. Using the MapReduce programming model, Hadoop can scale from a single computer system to thousands of machines that provide local storage and compute power. To increase storage capacity, simply add more servers to your Hadoop cluster.

Hadoop is made up of modules that interact to form the Hadoop framework. The following are some of the components that comprise the Hadoop framework:

Hadoop Distributed File System ( HDFS ) – This is Hadoop's storage unit. HDFS is a distributed document system which allows information to also be allocated across hundreds of computers with little loss in performance, allowing for huge parallel processing on commodity servers.
YARN (Yet Another Resource Negotiator) – YARN manages resources in Hadoop clusters. It manages batch, graph, interactive, and stream processes' allocation of resources and scheduling.
Apache HBase – HBase is a real-time NoSQL database that is primarily used for unstructured data transactional processing. HBase's flexible schema comes in handy when you need real-time and random read/write access to huge amounts of data.
MapReduce – MapReduce is a scheme for distributing data analytics jobs across multiple servers. It divides the input dataset into small chunks to speed up parallel processing with the Map() and Reduce() functions.

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Snowflake vs Hadoop:

When you've a general understanding of both technologies, we can compare Snowflake vs Hadoop on various parameters to better understand their strengths. We would then compare them using the following criteria:

  • Performance
  • Ease of use
  • Costs
  • Data processing
  • Fault tolerance
  • Security

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Performance:

Hadoop was originally intended to continuously collect data from multiple sources without regard for the type of data and store it across a distributed environment. This does very well. MapReduce is used for batch processing in Hadoop, and Apache Spark is used for stream processing.

Snowflake's virtual warehouses are its most appealing feature. This creates a separate workload and capacity (Virtual warehouse ). This allows you to separate or categorize workloads and query processing based on your needs.

Ease of use:

You can easily ingest data into Hadoop using [shell] or by integrating it with various tools such as Sqoop and Flume. The cost of deployment, configuration, and maintenance is perhaps Hadoop's most significant disadvantage. Hadoop is a complicated system that requires highly skilled data scientists who are familiar with Linux systems to use properly and concurrently.

This pales in comparison to Snowflake, which can be set up and running in minutes. Snowflake does not require any hardware or software to be installed or configured. Snowflake also makes it simple to handle/manage various types of semi-structured data, such as JSON, Avro, ORC, Parquet, and XML, through the use of native solutions.

Snowflake is indeed a database that requires no upkeep. It is completely managed by the Snowflake team, which eliminates maintenance tasks like patchworks and regular upgrades that would otherwise be required when running a Hadoop cluster.

Costs:

Hadoop was thought to be inexpensive, but it is actually a very costly proposition. While it is an Apache open-source project with no licensing fees, it is still expensive to deploy, configure, and maintain. You'll also have to pay a lot of money for the hardware. Hadoop's storage processing is disk-based, and it necessitates a large amount of disk space and computing power.

No need to deploy equipment or install/configure software in Snowflake. Although it has a cost, deployment and maintenance are simpler than with Hadoop. You pay for the following when you use Snowflake:Storage space is being used and the time spent querying data.

Snowflake virtual data warehouses could also be customized to "pause" when not in use to save money. As a result, the price per query estimate in Snowflake is substantially lower than in Hadoop.

Data Processing:

Hadoop is a fast way to batch process large static datasets (Archived datasets) that have been collected over time. Hadoop, on the other hand, cannot be used to run interactive jobs or analytics. This is due to the fact that batch processing does not enable companies to connect to changing business needs in real time.

Snowflake has wonderful service for both batch and stream processing, allowing it to function as both a data lake and a data warehouse. Snowflake provides excellent support for low latency queries, which many Business Intelligence users require, through the use of a concept known as virtual warehouses.

Computing and storage resources are disconnected in virtual warehouses. According to supply, you could even scale up or down on correlate or storage. Queries no matter how long have a size limit because computing power scales up with the size of the query, allowing you to get data much faster. Snowflake also includes built-in support for the most popular data formats, which you can query using the SQL query language.

Fault Tolerance:

Both Hadoop and Snowflake offer fault tolerance, but in various ways. Hadoop's HDFS is dependable and solid, and I've had very few issues with it in my experience.

Its horizontal scaling and distributed architecture offer high scalability and redundancy.

Snowflake also includes fault tolerance and multi-data center resiliency.

Security:

Hadoop provides security in a variety of ways. Hadoop provides service-level authorization to ensure that clients have the appropriate permissions for job submissions. It also provides standards from third-party vendors such as LDAP. Hadoop can also be encrypted. HDFS supports both conventional file permissions and ACLs (Access Control Lists).

Snowflakes are built to be safe. All information is secure while in transit, whether via the Internet or direct links, and while at rest on disks. Snowflake supports two-factor authentication as well as federation authentication with single sign-on. Authentication is predicated on a user's role. Policies can be enabled to limit access to predetermined client addresses.

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Hadoop Use Cases:

Because Hadoop's HDFS file system is not POSIX compliant, it is better suited for enterprise-class data lakes or large data repositories that require high availability and super-fast access. Another consideration is that Hadoop lends itself well to administrators who are familiar with Linux systems.

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Snowflake use cases:

Snowflake is the best choice for a data warehouse. Snowflake is the best option whenever you want to compute capabilities individually to manage workloads autonomously, since it offers individual virtual warehouses and great service for real-time statistical analysis. Snowflake stands out as one of the best data warehouses due to its high performance, query optimization, and low latency queries provided by virtual warehouses.

Snowflake, with its assistance for real-time data ingestion and JSON, is also an excellent data lake platform. It is ideal for storing large amounts of data while still being able to query it quickly. It is very dependable and supports auto-scaling on large queries, which means you only pay for the power you actually use.

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 Conclusion:

Snowflake, when compared to Hadoop, will allow users to produce deeper information from large, create significant value, and ignore lower-level activities if one's competitive advantage is delivering the product, solutions, or services.

Whenever it gets down to fully managed ETL, even so, there is no better alternative than snowflake, whether you'd like to keep moving your data into Snowflake or any other data warehouse.

It is indeed a No-code Data Pipeline which will assist you in transferring data from multiple sources to the destination of your choice. It's consistent and dependable. It includes pre-built implementations from over 100 different sources.

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Manikanth
Manikanth
Research Analyst
As a Senior Writer for HKR Trainings, Sai Manikanth has a great understanding of today’s data-driven environment, which includes key aspects such as Business Intelligence and data management. He manages the task of creating great content in the areas of Digital Marketing, Content Management, Project Management & Methodologies, Product Lifecycle Management Tools. Connect with him on LinkedIn and Twitter.