Apache Spark vs. Snowflake Comparison


Apache Spark Apache Software Foundation	Snowflake	+	+
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About Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources.	About Snowflake is a comprehensive AI Data Cloud platform designed to eliminate data silos and simplify data architectures, enabling organizations to get more value from their data. The platform offers interoperable storage that provides near-infinite scale and access to diverse data sources, both inside and outside Snowflake. Its elastic compute engine delivers high performance for any number of users, workloads, and data volumes with seamless scalability. Snowflake’s Cortex AI accelerates enterprise AI by providing secure access to leading large language models (LLMs) and data chat services. The platform’s cloud services automate complex resource management, ensuring reliability and cost efficiency. Trusted by over 11,000 global customers across industries, Snowflake helps businesses collaborate on data, build data applications, and maintain a competitive edge.
Platforms Supported Windows Mac Linux Cloud On-Premises iPhone iPad Android Chromebook	Platforms Supported Windows Mac Linux Cloud On-Premises iPhone iPad Android Chromebook
Audience Organizations that want a unified analytics engine for large-scale data processing	Audience Organizations interested in a powerful and comprehensive cloud data platform
Support Phone Support 24/7 Live Support Online	Support Phone Support 24/7 Live Support Online
API Offers API	API Offers API
Screenshots and Videos View more images or videos	Screenshots and Videos View more images or videos
Pricing No information available. Free Version Free Trial	Pricing $2/credit Pay for the compute and storage you actually use. - Turn compute resources on and off, so you only pay for what you use - Store near-unlimited amounts of data at affordable cloud rates - Grow your analytics infrastructure with linear cost scalability Free Version Free Trial
Reviews/Ratings Overall 0.0 / 5 ease 0.0 / 5 features 0.0 / 5 design 0.0 / 5 support 0.0 / 5 This software hasn't been reviewed yet. Be the first to provide a review: Review this Software	Reviews/Ratings Overall 4.8 / 5 ease 4.8 / 5 features 5.0 / 5 design 4.8 / 5 support 4.8 / 5 Read all reviews
	Pros & Cons from Real Users Pros Snowflake is being used across our whole organization. It addresses the need to combine disparate data sources into one single data source and discover insights into our business that would not be easily possible with lots of disconnected data sources. It has great SQL features like LISTAGG or Count Distinct, which go above and beyond Oracle and MS SQL. - Reporting queries run in a fraction of the time that they would in our production systems - We can run simple SQL queries. - Scales up and down seamlessly - Optimized table structures under the hood - Sharing data between accounts - Easy if use - Continuous data pipelines - Snowflake also includes caching at various levels to help speed up your queries and minimum costs. - Support members are very good. Snowflake is acting as a company-wide Data Repository. With its cloud architecture and scalability, it addresses our storage, warehouse, and computation problem at the same time. Snowflake has powerful group roles and policy, which makes it beneficial for enterprise edition and usage. Snowflake acts as a single platform for both data storage and warehousing needs. For deployment purposes, it has the best group policy management and the best UI I've encountered personally. It also accommodates direct connection with AWS and Azure, which is another advantage. - Hosted on cloud: Helps with scalability. - Handling large data scale and ingestion - Separation of compute and storage - Community is active and ever-growing: You will have someone from the community to help whenever you need it. Simple & clean interface Great querying performance Integration with all the major cloud providers Easy to learn querying language Good documentation (+) Easy to adapt and easy to use. (+) The run time of queries is much faster compared to other products in the market. (+) One can select a part of a query for execution. (+) Integration with S3 and other data storage platforms makes snowflake more compatible. (+) One may run concurrent queries without giving up on speed. Cons Nothing to dislike in Snowflake. Awesome data warehouse for huge data set. - Very expensive - Indexing by primary key is non performant Web based IDE is not suitable for writing complex queries, it feels sluggish when heavily used. (-) Performance drops in the case of multiple users. (-) No option for scheduling of a task or a query. (-) The coding syntax has some restrictions like the case-sensitivity issue.
Training Documentation Webinars Live Online In Person	Training Documentation Webinars Live Online In Person
Company Information Apache Software Foundation Founded: 1999 United States spark.apache.org	Company Information Snowflake Founded: 2012 United States www.snowflake.com
Alternatives dbt dbt Labs	Alternatives Teradata VantageCloud Teradata
TIMi	Google Cloud BigQuery Google
Snowflake	Amazon Web Services (AWS) Amazon
MLlib Apache Software Foundation	Databricks
PySpark View All	Narrative View All
Categories Big Data Data Analysis Data Modeling Query Engines Streaming Analytics	Categories AI Cloud Providers AI Infrastructure Big Data Columnar Databases Data as a Service (DaaS) Data Clean Room Data Exchange Data Governance Data Lake Data Management Data Marketplaces Data Monetization Data Preparation Data Science Data Warehouse Database Database Management Systems (DBMS) ETL LLM API Machine Learning OLAP Databases Query Engines SQL Databases
Show More Features Streaming Analytics Features Data Enrichment Data Wrangling / Data Prep Multiple Data Source Support Process Automation Real-time Analysis / Reporting Visualization Dashboards	Show More Features Data Warehouse Features Ad hoc Query Analytics Data Integration Data Migration Data Quality Control ETL - Extract / Transfer / Load In-Memory Processing Match & Merge Database Features Backup and Recovery Creation / Development Data Migration Data Replication Data Search Data Security Database Conversion Mobile Access Monitoring NOSQL Performance Analysis Queries Relational Interface Virtualization ETL Features Data Analysis Data Filtering Data Quality Control Job Scheduling Match & Merge Metadata Management Non-Relational Transformations Version Control
Integrations Actian Data Observability Alteryx Astro by Astronomer Dagster DataHub Foundational Genesis Computing Great Expectations Metabase Molecula Privacera Progress DataDirect Protegrity Rational BI Retina SQL Telmai Timbr.ai Union Cloud Vaultspeed Show More Integrations View All 186 Integrations	Integrations Actian Data Observability Alteryx Astro by Astronomer Dagster DataHub Foundational Genesis Computing Great Expectations Metabase Molecula Privacera Progress DataDirect Protegrity Rational BI Retina SQL Telmai Timbr.ai Union Cloud Vaultspeed Show More Integrations View All 975 Integrations
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