Snowflake vs Hadoop: 6 Critical Parameters

Organizations from different domains are investing in big data analytics. They are analyzing large datasets to uncover hidden patterns, unknown correlations, market trends, customer experiences, and other useful business information.

These analytical findings are helping organizations to have a competitive advantage over rivals through more effective marketing, new revenue opportunities, and better customer service.

Snowflake and Hadoop are two of the most prominent Big Data frameworks. If you are evaluating a platform for big data analytics, it is very likely that Hadoop and Snowflake are on your list or perhaps you are already using one of these systems. In this post, we’ll compare these two Big Data frameworks based on different parameters. But before you get into Snowflake vs Hadoop, it is important to get an overview of these technologies.

Introduction to Apache Hadoop

Hadoop is a Java-based framework that is used to store and process large sets of data across computer clusters. Hadoop can scale from a single computer system up to thousands of machines that offer local storage and compute power using the MapReduce programming model. To add new storage capacity, you just simply need to add more servers in your Hadoop cluster.

Hadoop is composed of modules that work together to create the Hadoop framework. The following are some of the components that work together to make up the Hadoop framework:

• Hadoop Distributed File System ( HDFS ) – This is the storage unit of Hadoop. HDFS is a distributed file system that allows for data to be spread across thousands of servers with little reduction in performance, therefore, enabling massively parallel processing using commodity hardware.
• Yet Another Resource Negotiator (YARN) – YARN handles resource management in Hadoop clusters. It handles the resource allocation and scheduling of batch, graph, interactive, and stream processes. 
• Apache HBase – HBase is a real-time NoSQL database that is mainly used for the transactional processing of unstructured data. The flexible schema in HBase is especially useful when you need real-time and random read/write access to massive amounts of data.
• MapReduce – MapReduce is a system for running data analytics jobs spread across many servers. It splits the input dataset into small chunks allowing for faster parallel processing using the Map() and Reduce() functions.

To learn more about Apache Hadoop, visit here.

Introduction to Snowflake

Snowflake is a modern cloud data warehouse that provides a single integrated solution that enables storage, compute, and workgroup resources to scale up, out, or down at the time of need to any level necessary. 

With Snowflake, there is no need to pre-plan or size compute demands months in advance. You just need to add more computing power either automatically or at the touch of a button.

Snowflake can natively ingest, store, and query diverse data both structured and semi-structured, such as CSV, XML, JSON, AVRO, etc. You can query this data with ANSI, ACID-compliant SQL in a fully relational manner. Snowflake does this with:

• No data pre-processing.
• No need to perform complex transformations.

This means that you can consolidate a data warehouse and a data lake in one system to support your SLAs with confidence. Snowflake provides both the flexibility to easily expand as your data and data processing needs grow as well as the ability to load data in parallel without impacting existing queries.

Learn more about Snowflake vs Salesforce.

Comparing Snowflake vs Hadoop

Now that you have an overview of both those two technologies, we can go ahead and discuss Snowflake vs Hadoop on different parameters to understand their strengths. We will compare them based on the following parameters:

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

Feature	Snowflake	Hadoop
Performance	Optimized for performance with automatic scaling, capable of handling large workloads efficiently.	Performance can vary based on cluster configuration and requires tuning for optimal results.
Ease of Use	User-friendly SQL interface; minimal setup required for users to start querying data.	Requires technical expertise to set up and manage, often involves more complex configurations.
Costs	Pay-as-you-go pricing model; costs can increase based on compute and storage usage but are predictable.	Generally lower storage costs, but infrastructure and operational costs can add up, especially with large clusters.
Data Processing	Supports both structured and semi-structured data; utilizes a columnar storage format for fast query performance.	Designed for processing large volumes of unstructured and semi-structured data using MapReduce and other processing frameworks.
Fault Tolerance	Built-in redundancy and failover mechanisms; automatically handles failures without user intervention.	Highly fault-tolerant; data is replicated across nodes to ensure no data loss during failures.
Security	Provides robust security features, including encryption at rest and in transit, and fine-grained access controls.	Security features depend on configuration; typically requires additional tools and settings for comprehensive security.

Snowflake vs Hadoop: Performance

Hadoop was originally designed to continuously gather data from multiple sources without worrying about the type of data and storing it across a distributed environment. It does this very well. Hadoop uses MapReduce for batch processing and Apache Spark for stream processing.

The beauty of Snowflake is its virtual warehouses. This provides an isolated workload and capacity (Virtual warehouse ). This allows separating or categorizing workloads and query processing according to your requirements.

Snowflake vs Hadoop: Ease of Use

You can ingest data in Hadoop easily either by using [shell] or integrating it with multiple tools like Sqoop and Flume. But perhaps the biggest drawback of Hadoop is the cost of deployment, configuration, and maintenance. Hadoop is complex and requires very sophisticated data scientists who are well versed with Linux systems to use properly and in parallel.

This compares poorly to Snowflake, which you can quickly set up and get running in minutes. Snowflake requires no hardware to deploy or software to install and configure. Snowflake also makes it easy to handle/manage the various types of semi-structured data such as JSON, Avro, ORC, Parquet, and XML using the native solutions that are provided.

Snowflake is also a zero-maintenance database. It is fully managed by the Snowflake team and this eliminates maintenance tasks such as patchworks and regular upgrades which you’d otherwise have to account for when running a Hadoop cluster.

Snowflake vs Hadoop: Costs

Hadoop was thought to be cheap, but it is actually a very expensive proposition. While it is an Apache open-source project that does not have licensing costs, it remains costly to deploy, configure, and maintain. You also have to incur significant TCO associated with the hardware. Storage processing in Hadoop is disk-based and Hadoop requires a lot of disk space and computing power.

In Snowflake, there is no need to deploy any hardware or install/configure any software. Although using it comes at a price, the deployment and maintenance are easier than with Hadoop. With Snowflake you pay for:

• Storage space used.
• Amount of time spent querying data. 

The virtual data warehouses in Snowflake can also be configured to “pause” when you’re not using them for cost efficiency. Given this, the price by query estimate becomes significantly lower in Snowflake compared to Hadoop. 

Snowflake vs Hadoop: Data Processing

Hadoop is an efficient way of batch processing large static datasets (Archived datasets) collected over a period. On the other hand, Hadoop cannot be used for running interactive jobs or analytics. This is because batch processing does not allow businesses to quickly react to changing business needs in real-time. 

Snowflake has great support for both batch and stream processing meaning that it can be used as both a data lake and a data warehouse. Snowflake offers great support for low latency queries that many Business Intelligence users need using a concept called virtual warehouses.

The virtual warehouses have decoupled storage and compute resources. You can scale up or down on compute or storage according to demand. Queries therefore no longer have a limit in size since the computing power scales up with the size of the query which means that you can get data much more quickly. Snowflake also includes built-in support for the most popular data formats which you can query using standard SQL dialect.

Snowflake vs Hadoop: Fault Tolerance

Hadoop and Snowflake both provide fault tolerance but have different approaches. Hadoop’s HDFS is reliable and solid, and in my experience with it, there are very few problems using it.

It provides high scalability and redundancy using horizontal scaling and distributed architecture.

Snowflake also has fault tolerance and multi-data center resiliency built-in. 

Snowflake vs Hadoop: Security

Hadoop has multiple ways of providing security. Hadoop provides service-level authorization which guarantees that clients have the right permissions for job submissions. It also supports third-party vendors like LDAP for authentication. Hadoop also supports encryption. HDFS supports traditional file permissions as well as ACLs (Access Control Lists).

Snowflake is secure by design. All data is encrypted in motion, over the Internet or direct links, and at rest on disks. Snowflake supports two-factor and federation authentication with a single sign-on. Authorization is role-based. You can enable policies to limit access to predefined client addresses. Snowflake is also SOC 2 Type 2 certified on both AWS and Azure and support for PHI data for HIPAA customers is available with a Business Associate Agreement.

Now let us look at the use cases where these technologies fit best.

Hadoop Use Cases

Since Hadoop’s HDFS file system is not a POSIX compliant file system, it is much more suited for enterprise-class data lakes, or large data repositories that require high availability and super-fast access. Another aspect to take into account is that Hadoop lends itself well to administrators that are well versed with Linux systems.

Snowflake Use Cases

Snowflake is best for a data warehouse. When you want to compute capacities separately to manage the workloads independently, Snowflake is the best option, because it provides isolated virtual warehouses and has great support for real-time data analysis. Virtual warehouses offer high performance, query optimization, and low latency queries to make Snowflake stand out as one of the best data warehousing platforms on the market today.

Snowflake is an excellent data lake platform as well, thanks to its support for real-time data ingestion and JSON. It is great for when you want to store bulk data while retaining the ability to query that data quickly. It is very reliable and allows for auto-scaling on large queries meaning that you’re only paying for the power you actually use.

Who Wins- Snowflake vs Hadoop?

Given the benefits of cloud data warehousing, you will at some point consider using a cloud data warehouse. While Hadoop has certainly fostered innovations in Big Data, it has also garnered a reputation for being complex to implement, provision, and use. Besides, the typical Hadoop data lake cannot natively provide the functionality you’d expect of a data warehouse. For example, Hadoop has: 

• No native support for SQL DML semantics like – UPDATE, DELETE and INSERT commands.
• No POSIX compliance.
• Extra complexity when working with relational data.

In contrast, Snowflake can limit the complexity and expense associated with Hadoop deployed on-premises or in the cloud. As such, only a data warehouse built for the cloud such as Snowflake can eliminate the need for Hadoop because there is:

• No hardware.
• No software provisioning.
• No distribution software certification.
• No configuration setup efforts are required.

Conclusion

Compared to Hadoop, Snowflake will enable you to deliver deeper insights from data, add more value, and avoid lower-level tasks when your core competency is delivering products, solutions, or services.

Compare Hadoop and SQL to see how they differ in terms of processing large datasets and query handling. Check out the detailed comparison in the Hadoop vs SQL guide.

However, when it comes to fully managed ETL, you can’t find a better solution than Hevo whether you want to move your data into Snowflake or any other data warehouse. It is a No-code Data Pipeline that will help you transfer data from multiple data sources to your chosen destination. It is consistent and reliable. It has pre-built integrations from 150+ data sources.

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