Data is more crucial to organizations than anything else. But how can businesses make the most of this opportunity? By deploying tried-and-true technology that power advanced analytics and BI solutions. Here's a quick rundown of two such technologies – Apache Hadoop and Snowflake – to help business owners figure out which is the best fit for their specific needs. Are you unable to decide which database platform is better -Snowflake vs Hadoop? Reading this Snowflake vs Hadoop article will definitely give you a clear vision of what is best. Here, we will discuss the comparison of these two Big Data frameworks Snowflake vs Hadoop using a variety of metrics in this blog. However, before proceeding with the comparison, it is necessary to gain a general understanding of these technologies.
Snowflake is a cutting-edge cloud data warehouse that offers a single integrated solution that allows storage, compute, and workgroup resources to scale up, out, or down as needed.
Snowflake is an analytic data warehouse that is available as Software-as-a-Service (SaaS). It provides companies with data warehouse capabilities that are faster, easier to use, and more adaptable than traditional data warehouse solutions. It's worth noting that Snowflake's data warehouse employs a brand-new SQL database engine with a cloud-specific architecture.
Snowflake is capable of ingesting, storing, and querying a wide variety of structured and semi-structured data, including CSV, XML, JSON, AVRO, and others. This data is fully relational and can be queried using ANSI, ACID-compliant SQL. This is how Snowflake does it:
This means you can confidently consolidate a data warehouse and a data lake into a single system to support your SLAs. Snowflake allows you to load data in parallel without interfering with existing queries.
Hadoop is an open-source framework developed at Yahoo and released as an open-source in 2012. Hadoop uses simple programming models to enable businesses to implement distributed processing of large data sets across clusters of computers. Hadoop can expand from a single computer system to thousands of computers with local storage and computation power using the MapReduce programming model. You just need to add extra servers to your Hadoop cluster to increase storage capacity.
Hadoop was created with the goal of allowing businesses to scale up from single servers to thousands of devices with local computation and storage. Businesses would be able to handle challenges involving large volumes of data and computing in this manner. It's no surprise that Hadoop has acquired a lot of attention as a prospective solution for data warehouse applications operating on expensive MPP appliances since 2012.
Now you have a general understanding of both technologies, we can compare Snowflake and Hadoop on many aspects to determine their capabilities. We'll compare them using the following criteria:
Snowflake can handle many read-consistent readings at the same time. It also allows for ACID-compliant changes.
Hadoop doesn't support ACID compliance, which means it writes immutable files that can't be updated or changed. Users must read a file in and write it out with the changes they have made. Hadoop isn't an excellent tool for processing ad-hoc queries because of this.
Snowflake's virtual warehouses are its most appealing feature. This creates a burden and capacity that is segregated (Virtual warehouse ). This allows you to separate or categorise workloads and query processing based on your needs.
Hadoop was created with the intention of continuously collecting data from a variety of sources without regard to the type of data and storing it in a distributed environment. This is something it excels at. Hadoop's batch processing is handled by MapReduce, whereas stream processing is handled by Apache Spark.
Snowflake uses variable-length micro-partitions to store data. It can handle small data sets as well as terabytes of data with ease.
Hadoop divides data into pre-defined blocks that are duplicated across three nodes. For small data files under 1GB, where the complete data set is normally stored on a single node, it is not a good solution.
Snowflakes can scale from a small to a massive data warehouse in a matter of seconds, and vice versa.
Hadoop is difficult to scale. Users can expand a Hadoop cluster by adding more nodes, but the cluster size can only be increased, not decreased.
Ease of use
This pales in comparison to Snowflake, which can be set up and running in minutes. Snowflake does not need the installation or configuration of any hardware or software. Using the native solutions given by Snowflake, it is also simple to handle/manage many types of semi-structured data such as JSON, Avro, ORC, Parquet, and XML.
Snowflake is likewise a database that requires no maintenance. It is fully managed by the Snowflake team, which avoids Hadoop cluster maintenance activities like patchworks and frequent updates that you would otherwise have to account for.
You may simply input data into Hadoop using [shell] or by connecting it with a variety of technologies such as Sqoop and Flume. The expense of implementation, configuration, and maintenance is likely Hadoop's worst flaw. Hadoop is difficult, and its appropriate and concurrent use needs highly skilled data scientists who are familiar with Linux systems.
There is no need to deploy any hardware or install/configure any software in Snowflake.
Although it is more expensive to use, it is easier to deploy and maintain than Hadoop.
You pay for the following using Snowflake:
Snowflake's virtual data warehouses may also be set to "pause" while you're not utilizing them to save money. As a result, Snowflake's price per query estimate is much cheaper than Hadoop's.
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 still expensive to deploy, configure, and maintain. You'll also have to pay a high total cost of ownership (TCO) for the hardware. Hadoop's storage processing is disk-based and therefore needs a lot of disc space and computer power.
Snowflake features excellent batch and stream processing capabilities, allowing it to serve as both a data lake and a data warehouse. Using a concept known as virtual warehouses, Snowflake provides excellent support for low latency queries that many Business Intelligence users want.
Storage and compute resources are segregated in virtual warehouses. According to demand, you can scale up or down on computation or storage. Because the computing power scales along with the size of the query queries no longer have a size restriction, allowing you to retrieve data considerably faster. Snowflake also comes with built-in support for the most common data formats, which you can query with SQL.
Hadoop is a solution for batch processing massive static datasets (Archived datasets) that have been collected over time. Hadoop, on the other side, cannot be utilised to conduct interactive jobs or perform analytics. This is due to batch processing's inability to respond effectively to changing business needs in real-time.
Both Hadoop and Snowflake provide fault tolerance, although their techniques are different.
Hadoop's HDFS is dependable and strong,
It uses horizontal scaling and distributed architecture to deliver high scalability and redundancy.
Fault tolerance and multi-data center resiliency are also integrated into Snowflake.
Snowflake is designed to keep your data secure. While in transit, whether via the Internet or direct links and while at rest on disks, all information is secure. Snowflake supports both two-factor and federation authentication, as well as single sign-on. The role of a user is used to authenticate them. Policies can be set up to restrict access to specific client addresses.
Hadoop protects data in a number of ways. Hadoop uses service-level authorization to verify that clients have the necessary permissions to submit jobs. It also includes third-party vendor standards, such as LDAP. Hadoop can be encrypted as well. Both traditional file permissions and ACLs are supported by HDFS (Access Control Lists).
Data is distributed across multiple machines as part of a cluster, and data can stripe and mirror automatically without the necessity of third-party software. It comes with the ability to stripe and mirror data.
For a data warehouse, Snowflake is the finest option. Because it offers individual virtual warehouses and excellent service for real-time statistical analysis, Snowflake is the perfect alternative whenever you wish to compute capabilities individually to handle workloads autonomously. Due to its high performance, query optimization, and low latency queries enabled by virtual warehouses, Snowflake stands out as one of the top data warehouses.
Snowflake is a great data lake platform since it supports real-time data ingestion and JSON. It's perfect for storing big volumes of data while also allowing for rapid queries. It's quite trustworthy, and it enables auto-scaling on huge requests, so you only pay for the resources you need.
Hadoop's HDFS file system is better suited for enterprise-class data lakes or big data repositories that demand high availability and super-fast access because it is not POSIX compliant. Another factor to consider is that Hadoop is well-suited to administrators with experience with Linux systems.
Hadoop is an excellent choice for a data lake, which is an immutable repository of raw business data. Snowflake, on the other hand, is a great data lake platform because it supports real-time data ingestion and JSON. Snowflake stands out as one of the top data warehousing platforms on the market today thanks to its high performance, query optimization, and low latency. Although it is more expensive to use, it is easier to deploy and maintain than Hadoop.
As a result, only a cloud-based data warehouse like Snowflake can eliminate the requirement for Hadoop because there is no hardware. There is no provisioning of software.
While Hadoop is the only platform for video, music, and free text processing, this is a small part of data processing, and Snowflake supports JSON natively, as well as structured and semi-structured queries from within SQL.
When compared to Hadoop, Snowflake allows customers to extract deeper information from large datasets, create significant value, and neglect lower-level activities if delivering products, solutions, or services is their competitive advantage. Even if you want to keep putting your data into Snowflake or any other data warehouse, there is no better option than Snowflake when it comes to completely managed ETL. It is, in fact, a No-code Data Pipeline that will help you transmit data from numerous sources to the destination of your choice. It's dependable and consistent. Pre-built implementations from over 100 distinct sources are included.
Stay updated with our newsletter, packed with Tutorials, Interview Questions, How-to's, Tips & Tricks, Latest Trends & Updates, and more ➤ Straight to your inbox!
|Snowflake Training||Oct 03 to Oct 18||View Details|
|Snowflake Training||Oct 07 to Oct 22||View Details|
|Snowflake Training||Oct 10 to Oct 25||View Details|
|Snowflake Training||Oct 14 to Oct 29||View Details|
Madhuri is a Senior Content Creator at MindMajix. She has written about a range of different topics on various technologies, which include, Splunk, Tensorflow, Selenium, and CEH. She spends most of her time researching on technology, and startups. Connect with her via LinkedIn and Twitter .
Copyright © 2013 - 2023 MindMajix Technologies