Databricks or Snowflake: a comparison of modern clouds

In data analysis and cloud computing, the modern clouds Databricks and Snowflake are leading innovation in the cloud.

Databricks promises unprecedented collaboration in data analysis, while Snowflake promises unparalleled scalability and optimized performance in data management.

Data analysis must be accessible to all teams in a company, and this can happen thanks to efficient data platforms.  

In this article, we'll explore the main features between Snowflake and Databricks, and you'll discover how these platforms can revolutionize your company's approach to data analysis and cloud computing.

Data lake, data warehouse, data cloud and delta lake:
data everywhere

The world of data is full of sometimes abstract concepts.

That's why we've put together a small dictionary with four data concepts and an explanation of where and how they can be stored.

So, first we introduce the most widespread concepts, data lake and data warehouse, in the traditional order of data flow.

Next, we'll talk about two more specific and widespread concepts from Snowflake and Databricks: data cloud and delta lake.

1- Data lake

Data lake stores relational data from business applications, as well as non-relational data (such as data from apps, IoT devices, and social media).

One of the main differentiators of the data lake is that the data structure or schema is not defined when the data is captured.

In this way, they can be stored without careful design or without being clear about the business questions that the data will help to answer in the future.

2- Data warehouse

Data warehouse is a central repository of data that enables better decision-making.

Data in the data warehouse comes from transactional systems, relational databases and other sources.

Business and data users, such as analytics engineers, data analysts and data scientists) can access the data to create reports and analyses.

The main difference between a data warehouse and a transactional database is the way the data is stored and queried.

In the data warehouse, queries are columnar to facilitate searching in BI tools, for example.

In the transactional database, queries typically occur in rows.

3- Data cloud

Snowflake is considered a data cloud because it has many features that go beyond simple data storage and processing, such as a marketplace and data products.

A data cloud identifies cloud solutions that store and process data, eliminating data silos and enabling fluid integration between companies' storage and processing needs in order to transform data into monetizable resources .

In other words, data cloud is part of the concept of cloud computing and goes beyond a simple data warehouse.

4- Delta lake

Lastly, a concept that Databricks has spread is delta lake, which is the platform's standard storage format.

Delta lake refers to the optimized data storage layer and the tables built on top of a data lake.

This is open source software that is compatible with Apache Spark APIs and provides aspects of ACID transactions (atomicity, consistency, isolation and durability), as well as handling scalable metadata and unifying batch and streaming processing.

There are many data cloud solutions on the market today with differentiating features that can help your company to mine the value of existing data in a faster, more scalable and more secure way.

Two important solutions are:

1. Snowflake - a high-performance data warehouse;
2. Databricks - an advanced performance delta lake for big data.

Databricks and Snowflake: the state of the art of modern clouds

Databricks is a unified and highly scalable cloud analytics platform that combines essential functionalities for processing and handling large volumes of data.

Snowflake is an advanced cloud data platform that allows you to store, process and analyze data in a fast, flexible and scalable way.

Let's get to the details!

Databricks

With a favorable environment for the development of data analysis, machine learning and artificial intelligence activities, Databricks provides a collaborative and interactive environment for the delivery of complete solutions.

Based on Apache Spark, one of the most popular tools for processing big data this platform not only manages to deliver computing power on a large scale, but is also extremely efficient in terms of scalability.

With the help of its dynamic cluster management system, Databricks is able to optimize processing based on workload, reducing costs and increasing performance.

In addition, this platform adopts a hybrid approach that combines the best elements of the concepts of data warehouse and data lake into an innovative data storage solution called delta lake.

This solution enables the management and storage of a wide variety of data types, from raw data to transformed data ready for analysis.

Snowflake

One of its distinguishing features is that it is a self-managing platform. This means:

• there is no need to configure hardware (physical or virtual),
• no need to install complicated software,
• or managing and maintaining complex data infrastructures.

Because it runs completely on a cloud infrastructure, this platform allows to quickly and easily deliver its full potential value, removing the need for highly specialized professionals and being a more affordable solution.

It's important to note that Snowflake is built on other cloud services, such as AWS, Google Cloud Platform or Azure.

This makes it a multi-cloud data warehouse solution that makes the most of the multiple clouds on the market.

In addition, Snowflake offers seamless integration with business intelligence (BI) tools, making it easy to create interactive dashboards and customized reports.

Its use cases include business analysis, data science and real-time data processing.

Data analysis and cloud computing

Databricks and Snowflake have varied approaches to data analysis and cloud computing, catering to different needs and use cases.

So let's talk about the characteristics of both, their processing performance and their pricing structures.

Features

Databricks

It offers advanced machine learning and artificial intelligence capabilities in the cloud, enabling the development of complex models and the implementation of automated data pipelines.

• Unified data platform: centralizes in a single platform the tools needed for the activities of data engineering, data science, data analytics e machine learning. This ensures greater interaction between professionals working on data solutions.
• Interactive workspace: allows you to use a wide variety of tools and languages available on the market. You can use Python, R, Scala and SQL as well as being able to use Jupyter Notebooks natively.
• Multicloud: located within the company's cloud infrastructure. Among the clouds available for use are AWS, Azure and Google Cloud Platform.
• Parallel processing: distributes computing tasks simultaneously. All the tasks used within the tool are linked to sparse clusters, which can be configured and scaled according to computational needs.
• Optimized storage: uses a storage layer called delta lake, which combines the reliability and performance of data warehouses with the scale and flexibility of data lakes. It provides optimized, performance-efficient storage. And it offers options for managed tables, managed by the delta lake, and lower-cost external tables.

• Data governance: offers the concept of Unity Catalog, a tool aimed at improving data governance, security and discovery. It allows you to easily manage data access, discovery and compliance at different data sources within the platform.

• Pay-as-you-go: you pay according to the use of processing resources, which are measured in Databricks Units (DBUs). This cost model provides flexibility and control, and allows companies to scale resources as needed, paying only for the DBUs used and the time the cluster is on.

Snowflake

It uses a shared cloud data warehouse architecture, allowing several organizations to access the same resources in isolation.

Because it is built on top of other cloud services, it is a multi-cloud data warehouse solution that acts as an intermediary, absorbing risks and optimizing storage and processing.

In addition, it features a more intuitive implementation with fluid integration with BI services and data extraction, such as Fivetran.

And some of Snowflake 's main features are:

• high computing performance: fast query speeds and data warehouse scaling to accommodate data processing peaks.
• Simplified data warehouse: Snowflake 's main focus is to provide an easy-to-implement, completely cloud-based cloud solution. As a data warehouse, it can manage both structured and semi-structured data, such as Json, Avro, XML, among others.
• architecture: this shared cloud data warehouse separates storage from processing, which allows for independent scaling and an optimized cost structure.
• scalability: allows processing to be increased without affecting data storage performance. It has independent scalability between storage and processing.
• pricing: separate cost structure for computing(pay-per-query) and storage (charged per terabyte of monthly storage).
• security and compliance: has a strong focus on security and compliance, allowing role-based access to control users and permissions, as well as support for various compliance standards.
• marketplace: it has a data marketplace, data services and native apps that help companies find solutions to their most diverse digital needs.
• integrated services ecosystem: Snowflake 's platform currently has several certified partners that allow access to the platform through a vast network of connectors, drivers, programming languages and utilities. Snowflake 's partnerships in the platform ecosystem can be classified into six different categories:

1. data integration: dbt, Tableau, Fivetran and Stitch;
2. machine learning and data science: Alteryx, SAS, and Databricks itself;
3. security and governance: Alation, Hunters and data.world;
4. business intelligence: Power BI, Tableau, Looker and Qlik;
5. SQL editors: DBeaver and Snowsight (UI);

programming interfaces: Python, SQL Alchemy and .net.

Processing performance and scalability

Databricks

Databricks offers high processing capacity. One of its main differentials is the use of Apache Spark for distributed data processing.

Even in heavy workload scenarios, Databricks can scale quickly to meet demand while maintaining consistent performance.

To do this, the concept of clusters is used, groups of machines powered by Apache Spark provisioned to carry out data processing tasks.

Each cluster is configurable, allowing you to choose the amount of memory and the number of CPU cores for each node, as well as the total number of nodes.

Snowflake

What sets Snowflake apart is the separation of storage and processing, which allows for fast scalability and consistent performance.

So even in heavy load situations, such as during a massive marketing campaign for your company, Snowflake performs well both for storing new records and for processing queries.

And by relying on serverless computing and intuitive implementation, Snowflake 's platform allows customers to benefit from computing elasticity that expands resources when needed, without having to manually connect new clusters or contract individual services.

Snowflake thus automatically manages how computational tasks are executed.

Another interesting feature of Snowflake is its ability to handle streaming data (as well as batch data), which makes it an excellent tool for operations with high data flows.

Billing structure

Databricks

Databricks has a billing model focused on data processing. However, unlike other tools that charge by volume processed, uses the concept of Databricks Units (DBUs), a processing unit that is charged per second of use.

The cost of processing, then, is based on a number of factors:

• subscription type: offers different subscription types, such as Standard and Premium, which offer different DBU prices.
• instance type: offers a good variety of processing units, capable of supporting everything from low loads to extremely heavy computing activities(big data).
• number of DBUs: some processes require a greater number of concurrent processing units to handle parallel activities. The greater the number of DBUs active at any one time, the higher the final cost.
• uptime: the cost is calculated by the fraction of a second of the instance. In this way, the price is not formed by the processing time, but by the time the cluster is connected.

You can simulate different Databricks implementation configurations here to estimate costs for your company.

Snowflake

Snowflake operates on a granular pricing model, charging separately for data storage and processing through credits purchased by users.

This is how the cost structure is carried out:

• processing usage: charging for computing resources used to execute queries in the database(pay-per-query);
• storage usage: calculation independent of processing; storage pricing is calculated according to the volume of terabytes of monthly data stored; Snowflake uses compression and data storage optimization to reduce costs.

You can find out here how much it costs to implement Snowflake in your business.

Indicium can help you compare and choose

Databricks or Snowflake?

Indicium is a data company in New York and Brazil. We are specialized in creating solutions in data science, analytics and artificial intelligence.

We want to help you make smarter decisions based on your business data.

And we can start by clearing up all your doubts about how to choose between a data warehouse and a delta lake, or rather, between Snowflake and Databricks.

Save your time. Come and talk to us by clicking here.