Databricks vs. Redshift: Which one is best in 2024?
Struggling to choose between Databricks vs. Redshift? Learn the key differences, helping you choose the best data warehouse for your needs in 2024.
Searching for an accurate and fair comparison between Databricks and Redshift? You’re in the right place.
In this comparative piece, we’ll settle the Databricks vs. Redshift debate by looking at their feature offerings, their shortcomings, and which one you should choose for your data handling needs.
To save you valuable time, we’ll kick things off with a quick TL;DR comparison.
Databricks vs. Redshift: At a glance
Here's a comparative chart that summarizes the key aspects of Databricks and Redshift:
Databricks
Databricks has carved a niche for itself as a unified analytics platform. Think of it as your one-stop shop for all things data: engineering, science, analytics, and even machine learning. It's like having a Swiss Army knife for your data needs, but much more elegant.
Built on the foundation of Apache Spark (the open-source data processing engine that's all the rage), Databricks offers a collaborative environment where everyone from your meticulous data engineers to your insightful business analysts can work together.
This means no more silos, no more data getting lost in translation. Everyone's on the same page, working towards the same data-driven goals.
Databricks features
Databricks isn't just another data platform; it's a Lakehouse — a concept that marries the best of data lakes and data warehouses. This means it can handle the raw, unstructured data of a lake while also providing the structure and query capabilities of a warehouse. It's like having your cake and eating it too, but in the data world.
Delta Lake
At the heart of Databricks lies Delta Lake, its open-source storage layer. Delta Lake acts like a fortress, ensuring your data's reliability and consistency. It introduces ACID transactions (Atomicity, Consistency, Isolation, Durability) to your data lake, which basically means your data is safe, sound, and always ready for action.
Think of it like having a safety net for your data. Even if something goes wrong during processing, your data remains intact, and you can easily roll back to a previous state. No more sleepless nights worrying about data corruption or inconsistencies.
Notebooks
Databricks takes collaboration to the next level with its interactive notebooks. These notebooks are like virtual whiteboards where your team can work together, sharing code, visualizations, and insights in real time. Whether you're using Python, Scala, SQL, or R, Databricks has got you covered.
This collaborative approach means faster development cycles, better knowledge sharing, and, ultimately, more impactful data-driven decisions. It's like having a brainstorming session with your team, but with the added power of Databricks' robust platform.
Machine learning
Databricks allows you to build, train, and deploy machine learning models with ease thanks to its wide range of machine learning libraries and tools at your disposal.
Whether you're a seasoned data scientist or just starting your machine learning journey, Databricks provides a user-friendly interface and powerful capabilities to unlock the hidden potential of your data.
Databricks’ pricing
Databricks follows a pay-as-you-go pricing model, which means you only pay for what you use. This is great news for businesses of all sizes. They also offer committed-use discounts for those who are in it for the long haul.
But remember, the total cost can depend on several factors, including the cloud provider you choose (AWS, Azure, or GCP), the size of your clusters, and the amount of data you're processing.
So, it's always a good idea to do your homework and estimate your costs before diving in.
Who is Databricks good for?
While Databricks is a powerful platform, it's not for everyone. Here's a quick rundown of who would benefit most from it:
- Data teams of all sizes: Whether you're a small startup or a large enterprise, Databricks can scale to meet your needs.
- Organizations embracing the cloud: Databricks is cloud-native, so if you're already in the cloud or planning to move there, it's a natural fit.
- Companies with diverse data needs: Databricks can handle structured, semi-structured, and unstructured data, making it versatile for various use cases.
- Businesses prioritizing cross-team collaboration: If you want a platform that fosters teamwork and knowledge sharing, Databricks excels in this area.
Redshift
Amazon Redshift is a trusted and reliable data warehouse solution. It operates as a fully managed service within the vast Amazon Web Services (AWS) ecosystem.
This means you can let Amazon handle the nitty-gritty of managing the infrastructure, freeing you up to focus on what truly matters — analyzing your data and extracting valuable insights.
Redshift features
Redshift is about enabling you to make sense of your data. It achieves this through a combination of features that have been honed over the years:
Columnar storage and MPP architecture
At the core of Redshift's performance lies its columnar storage and massively parallel processing (MPP) architecture. This combination lets Redshift rapidly process complex queries on massive datasets.
Think of it like having a team of experts working on different parts of a puzzle at the same time, drastically reducing the time it takes to complete the picture. This makes Redshift a natural choice for analytical workloads, where speed and efficiency are paramount.
Redshift’s AQUA
Redshift takes things up a notch with its Advanced Query Accelerator (AQUA). This feature acts like an accelerator for your queries, making them up to 10 times faster than other cloud data warehouses. If you're dealing with time-sensitive analytics, AQUA can make a real difference.
Easy integration with the AWS ecosystem
One of Redshift's standout features is its integration with other AWS services. This means you can easily connect Redshift to your existing data pipelines, storage solutions, and security tools within the AWS ecosystem.
Redshift’s pricing
Amazon Redshift also follows a pay-as-you-go pricing model, which means you only pay for the resources you consume. You have the flexibility to choose between hourly rates based on cluster size and usage, or opt for data scanning charges.
To sweeten the deal, Redshift also offers volume discounts for long-term contracts, making it a cost-effective option for businesses committed to using it for the long haul.
Who is Redshift good for?
Here's a quick look at who would benefit most from Redshift:
- Enterprises with large datasets: If you're dealing with massive amounts of data, Redshift's scalability and performance can handle the load.
- Organizations entrenched in AWS: If you're already heavily invested in the AWS ecosystem, Redshift integrates seamlessly with other services.
- SQL-savvy teams: Redshift is built for SQL, so if your team is comfortable with this language, you'll feel right at home.
- Businesses prioritizing BI: If your primary focus is business intelligence and reporting, Redshift is optimized for this type of workload.
Databricks vs. Redshift: Which should you choose?
Let's break down the key considerations to help you make an informed decision:
Your data needs
- Do you primarily work with structured data for business intelligence and reporting?
Redshift, with its SQL prowess and long-standing expertise in data warehousing, might be your go-to. It's designed for those who know their way around SQL and want a platform that excels in handling large datasets for analytical purposes.
- Do you have a mix of structured, semi-structured, or even unstructured data that you need to process for various use cases, including machine learning and data science?
Databricks, with its Lakehouse architecture and versatility, could be the better fit. It's like a playground for data professionals who want to experiment, innovate, and push the boundaries of what's possible.
Your team's expertise
- Is your team well-versed in SQL and comfortable with a more traditional data warehousing approach?
Redshift's familiar SQL interface and integration with popular BI tools might be the smoother path.
- Are you working with data scientists and engineers who are proficient in languages like Python, Scala, or R?
Databricks' collaborative notebooks and extensive machine-learning libraries could be a dream come true for your team.
Your cloud strategy
- Are you already deeply invested in the AWS ecosystem?
Redshift's seamless integration with other AWS services could be a major advantage, providing a unified experience and streamlined workflows.
- Do you prefer a multi-cloud approach or want to keep your options open?
Databricks' ability to run on AWS, Azure, and Google Cloud Platform gives you the flexibility to choose the best cloud provider for your specific needs.
Your budget
- Are you looking for a cost-effective solution that scales with your usage?
Both Databricks and Redshift offer pay-as-you-go pricing models, allowing you to control your costs based on your actual usage. Do keep in mind that the total cost can vary depending on factors like data volume and compute resources
Integrating with Eppo
Eppo is a powerful platform for experimentation, and integrating it with your data warehouse — whether it's Databricks or Redshift — opens up a world of possibilities for data-driven decision-making.
But what sets Eppo apart from other experimentation platforms?
Unlike traditional experimentation platforms that often require complex integrations and data pipelines, Eppo easily connects to your existing data infrastructure.
This means you can leverage the power and security of your chosen data warehouse, whether it's the versatile Databricks Lakehouse or the robust Redshift data warehouse, to run experiments without disrupting your existing workflows.
Eppo's warehouse-native approach offers several key advantages:
- Data centralization: All your experiment data resides in your warehouse, eliminating the need for additional data storage and simplifying analysis.
- Real-time insights: Eppo leverages your warehouse's processing capabilities to deliver experiment results quickly, enabling you to iterate and make data-driven decisions faster.
- Enhanced security: Your sensitive experiment data stays within your secure warehouse environment, reducing the risk of data breaches or leaks.
- Scalability: Eppo can scale alongside your data warehouse, accommodating experiments of any size.
Now that you know why the fact that Eppo is warehouse-native is such a big deal, let’s see how to connect Eppo to these two data warehouses:
Connecting Eppo to Redshift
1. Prepare your Redshift warehouse
- Add Eppo's IP addresses to your cluster's security group allowlist. This allows Eppo to communicate with your Redshift database.
- Create a dedicated user (e.g., "eppo_user") and grant it SELECT permissions on the relevant tables. This ensures Eppo has access to the data it needs without unnecessary privileges.
- Create an output schema (e.g., "eppo_output") where Eppo can write intermediate results and temporary tables.
2. Gather connection details
- Note your Redshift cluster's endpoint (found in the Connection details section of the Properties tab).
- The default database port is 5439.
- Record your Redshift database name.
- If using an SSH tunnel, gather the necessary details (tunnel host, username, password/public key).
3. Configure credentials in Eppo
- Log into your Eppo account and navigate to the data warehouse connection screen.
- Choose the Redshift tab and enter the connection details you gathered.
- Click "Test Connection," and if successful, save your settings.
Connecting Eppo to Databricks
1. Prepare your Databricks workspace
- Create a service principal (e.g., "Eppo Service Principal") and an Eppo account group (e.g., "Eppo Service Group").
- Add the Eppo Service Group to your workspace and enable Databricks SQL access and Workspace access.
- Allow Personal Access Token usage for the Eppo Service Group.
- Create an output catalog (e.g., "eppo_service_catalog") and schema (e.g., "eppo_output") for Eppo.
- Grant the Eppo Service Group SELECT privileges on the relevant catalogs, schemas, and tables.
2. Gather connection details
- Locate the host and path information from your Databricks SQL warehouse connection details.
- Generate an access token for the Eppo Service Principal using Databricks' API.
- Copy the names of the output catalog and schema you created.
3. Configure credentials in Eppo
- Log into your Eppo account and navigate to the data warehouse connection screen.
- Choose the Databricks tab and enter the connection details you gathered.
- Click "Test Connection," and if successful, save your settings.
A note on security
Eppo takes security seriously and uses Google Secret Manager to store and manage your credentials. This ensures your sensitive information is protected and not stored in plain text.
Updating credentials
You can easily update your credentials in Eppo's Admin panel at any time.
Next steps
Once you’ve decided on the data warehouse you’ll use, you’ll be ready to start connecting Eppo to it. You’ll be able to track and analyze metrics that are actually trustworthy.
This is because Eppo is warehouse-native, meaning you’re always pulling data from your internal source of truth. Curious about how Eppo can help you run experiments that impact your key business metrics such as retention and profit margins?
Struggling to choose between Databricks vs. Redshift? Learn the key differences, helping you choose the best data warehouse for your needs in 2024.
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