When thinking about cloud computing and big data processing, most people either think of Snowflake or Spark. These are different types of computing models in Big data and Hadoop platforms. Spark is a general framework that does everything from real-time analysis to interactive data processing including SQL. And Snowflake focuses exclusively on scalable data warehousing through streaming ingestion, business intelligence (BI) to ETL (Extract-Transform-Load), disaster recovery, security, and administration.
These two clouds have completely different design goals. Knowing the difference between them can help you pick the right cloud for your needs and prevent software lock-in. Check out the further sections of this article for more information on the comparison between Spark and Snowflake.
Snowflake vs Spark - Table of Content
What is Snowflake?
Snowflake is a cloud-based SQL data warehouse that focuses on top-notch performance, zero-tuning, diversity of data types, and security. It is a data storage and analytics service generally termed as "data warehouse-as-a-service". The platform combines the power of data warehousing, the flexibility of big data platforms, and the elasticity of the cloud at a fraction of the cost of traditional solutions.
Snowflake Components
The Snowflake architecture supports multiple data warehouse use cases using a single, integrated platform that is fast, flexible, and secure.
Snowflake delivers:
- Relational database (ANSI SQL) semantics
- Fully relational queries (e.g., JOINs), transactions, and DML operations
- True SQL support (e.g., window functions)
- Data warehouse features (e.g., different schemas for each stage of processing).
Advantages of Snowflake
The following are the advantages of Snowflake:
- Using Snowflake, one can create multiple clusters as per the need and can also set the size of each cluster.
- Snowflake allows storing semi-structured data like JSON, Avro, ORC, Parquet, XML in a structured manner.
- There’s no need to tune the performance of queries. The system automatically detects the workloads and prioritizes them accordingly.
- Snowflake is ACID compliant. So, users can use this tool for all transaction-related operations.
- Snowflake has a very flexible pricing policy. You only pay for what you use and nothing more than that. So, no upfront cost or long-term commitments are required to use this tool.
Disadvantages of Snowflake
The following are a few disadvantages of Snowflake:
- Snowflake's flagship product is the cloud-based data storage platform. It doesn't support on-premise deployment.
- Relatively new products and product characteristics are constantly changing and updating, there may be some version differences in the future.
What is Spark?
Apache Spark is an open-source cluster computing framework. It can handle both batch and real-time analytics and data processing workloads.
Spark lets you spread data and computations over clusters with multiple nodes (think of each node as a separate computer). With this functionality, each node in Spark works with a small amount of data. In other words, the data gets distributed among multiple nodes. Hence, working with larger datasets becomes easier with Spark.
Apache Spark Components
Apache Spark has three main components: Spark Core, Spark SQL, and MLlib (machine learning library).
- Spark Core: The Spark core is responsible for scheduling, distribution, and fault recovery. The rest of the components are built above this core layer to provide added functionality.
- Spark SQL: This library allows you to query structured data as a distributed dataset (RDD) in Spark, with integrated APIs in Java, Scala, Python, and R.
- MLlib: Instead of reading data from disk into memory and then operating on it, Spark loads the data into memory and operates on it directly. This enables its ability to run all the programs at superfast speed.
Spark also uses an abstraction called RDDs (or Resilient Distributed Datasets) to store information. RDDs are immutable collections of objects which are split across multiple machines so they can be processed in parallel. Once you create an RDD you can keep it in memory across parallel operations. So, you don’t have to read from a disk each time you want to use it.
[ Check out Apache Spark Features ]
Advantages of Apache Spark
The following are the advantages of Apache Spark:
- Easy to use and learn.
- Interactive shells.
- Spark can process data that is present on disk or memory or both, so it can handle real-time streaming data too.
- It supports different languages like Java, Scala, Python, R, etc. Using these languages with Spark, one can easily create different applications such as batch processing, iterative algorithms, etc.
- Ability to process both batch and streaming data.
Disadvantages of Apache Spark
The following are some disadvantages of Apache Spark:
- Debugging with Spark is a bit difficult because it is not very intuitive sometimes.
- It can be expensive because you need to write extra codes to get things done with spark. You also need to hire developers with spark skills which can be difficult to find if you’re not in the tech industry.
[ Check out Apache Spark Challenges ]
Snowflake vs Spark - Differences
| Category | Snowflake | Spark |
| Main difference | Snowflake is a software-as-a-service (SaaS) platform for data storage, analytics, and data warehousing. | Spark is an open-source framework where large datasets are divided into smaller components for easier data processing. |
| Data structure | Snowflake stores structured data in tables that can be queried using SQL. It uses a relational model, so it's easy to migrate from other relational databases like Oracle or MariaDB. Snowflake supports semi-structured data including JSON and Avro files as well as XML, ORC, and Parquet files. | Spark uses Resilient Distributed Datasets (RDDs), which are in-memory collections of objects. An RDD contains all the data required to carry out computations across many different machines. |
| Scalability | Snowflake contains a multi-cluster, shared-data architecture in the cloud. This makes it highly scalable and elastic. It can scale up or down to any size in seconds with no downtime, cost, or hassle. | Apache Spark scales up or down by adding or removing nodes from the cluster to increase or decrease memory and processing power. This allows it to process extremely large datasets across thousands of nodes for batch processing jobs that take hours or even days to complete. |
| Architecture | Snowflake is a data warehousing platform with multiple clusters that can be integrated into a single cloud. There are three major layers viz. storage layer, computing layer, and cloud servicing layer. The data gets stored and analyzed within these layers. | Spark is an open-source data processing framework for executing parallel and distributed computations. Spark provides APIs in Java, Scala, Python, R, and SQL. |
| Security | Snowflake provides support for encryption with automatic key management through AWS Key Management Service (KMS). | Spark provides the ability to run SQL queries on the structured data in real-time through SparkSQL APIs. |
| Performance | Snowflake is designed to perform very fast at scale; therefore, you don’t need to worry about tuning parameters or managing indexes for performance reasons like you would with other databases. | Spark processes a large amount of data faster by caching it into memory instead of disk storage because disk IO operations are expensive. |
| Data source | Snowflake can ingest data from various sources including S3, Azure Blob, or ADLS Gen2. | Spark is a cluster computing platform that can ingest data from multiple sources including HDFS. |
| Data storage | Snowflake supports automatic schema detection, schema alteration, and schema evolution of JSON files. It supports nested data types such as arrays, maps, and structs allowing you to easily query semi-structured data without flattening it out into relational format. | Spark uses its own internal metadata storage which can be configured using Hive metastore database or Derby database. It also provides the feature of dynamic partition pruning when working with partitioned tables. |
| Use cases | Snowflake is used in large datasets such as warehousing (ETL), business intelligence (BI), and Snowflake Elastic Data Warehouse (SEDW). Snowflake is designed to handle relational data warehouse queries. |
Spark can be used for data processing, machine learning models, and even for graph processing algorithms. It is a very versatile tool in the big data world but it is not a database. Spark is designed to handle batch processing, real-time stream processing, machine learning, and interactive queries. |
Spark vs Snowflake - Conclusion
In conclusion, both Spark and Snowflake are NoSQL databases and they are both open-source. However, they differ with respect to the data model they use (relational vs. graph), scale-up features, and flexible querying capabilities. Spark offers very strong support for streaming, including the ability to stream lineage, and supports in-memory batch processing. It is a user-friendly platform, attracting a large community of developers. While Snowflake can be used to process large amounts of set data, it can integrate with a variety of applications and data sources.
The key difference between Spark vs Snowflake is that Snowflake is designed primarily for analytics processing, while Spark is used for batch processing and streaming capability. Hence, the choice needs to be made based on your requirements!
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Liam Plunkett
Solution Architect
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