When it comes to data analytics, business users and data scientists often have very different priorities. Data analysts need fast, reliable tools to explore their data and build models efficiently. Business users want self-serve tools that make it simple to run analyses and produce visualizations. Depending on your team’s specific needs, choosing the right tool is a tricky balance between cost, ease of use, performance, and the scope of features you’ll need for your project. In this article, we’ll compare Databricks vs Sagemaker so you can decide which tool is the best fit for your team.
Main Differences between Databricks vs Sagemaker summarized
| Databricks | Sagemaker |
| Offline support during Business Hours | Online support |
| Training through webinars and documentation | Live online training |
| Integrates with Amazon web services but no Amazon A2I, EC2, or Redshift | Integrate with everything but Acceldata, nor Assure Security |
| Share workspaces and deep dive analysis | No deep model building |
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Databricks vs Sagemaker: Features & Capabilities
Databricks
Databricks uses the Lakehouse platform to allow business analysts access to data warehousing and AI use cases all in one place, eliminating those time-consuming and complicated data silos. Combine this with their AI and Databricks mission is to help all data teams solve the toughest problems.
Features
- Maximizes flexibility through open source and open standards foundations
- Delta Lake provides the reliability and performance, breaking down barriers and streamlining your workflow
- 450+ partners across the data network with unrestricted access to open-source data projects
- Multicloud synthesis for your teams. Your processes remain the same no matter what
- Cloud-based data projects are ideal for data warehousing, real-time monitoring, and data governance
- Photon engine is powerful and fast enough for deep-dive analysis that requires the execution of highly complex queries while collaborating across your company
- Extra support for learn-as-you-go machine learning
Sagemaker
As part of Amazon’s AWS services, Sagemaker addresses the tougher aspects of machine learning to make it easier for companies to develop high-quality models with a single toolset. Your developers and data scientists can efficiently build, train, and implement machine learning models at any scale.
Features
- Data Wrangler connects your data sources and uses built-in transformations to engineer your model features quickly and easily
- Clarify automatically detects bias in your data during prep and again after training, adding data in to fix it. It also writes explainability reports for your stakeholders, so they know how and why your model works.
- Fully secure platform for your machine learning environment
- No more creating your own labeling applications or workforces. Ground Truth Plus provides its own workforce for accurate training datasets and manages the workflow simultaneously
- No-code machine learning environment with point-and-click models. No code writing skills or machine learning experience is necessary
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Databricks vs Sagemaker: Pricing
Databricks Pricing
Databricks is a pay-as-you-go service. You pay for the compute resources you use which are calculated on a per-second granularity. How much per second varies with the services you choose. They also offer savings and discounts if you commit to a certain level of usage. As with most things, the larger amount you commit to, the more your savings. These data commitments can be used across multiple cloud platforms. They also offer a 14-day free trial on your cloud service.
Sagemaker Pricing
Amazon Sagemaker has a limited free tier and for those interested in paid subscriptions, they have a great tool on their website for calculating an estimate for your company (link above). They also provide the pay-per-use pricing points for different services and examples like Data Wrangler at $0.922 per hour. If you used Data Wrangler for 18 hours over 3 days, it would cost you $16.596.
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Reasons to choose Databricks over Sagemaker
If you want a cloud service that’s fast, efficient, and can handle large datasets, then Databricks is your choice. It’s specifically designed to accelerate innovation projects. Simpler operations and reduced costs come from it running Spark in the background, their original software (Apache Spark). Bonus, it runs and configures a Spark environment for you, you don’t have to configure it yourself.
Reasons to choose Sagemaker over Databricks
Sagemaker works efficiently and quickly with other tools on the Amazon ecosystem. If you use Amazon Web services already, Sagemaker is a good choice for you. You can choose multiple servers to train your machine learning models, and all data and projects are stored in S3. This software has taken large steps in making data mining and machine learning more user-friendly making it ideal for businesses that want to use machine learning for market predictions, call-center efficiency, and predictive analytics.
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Databricks and Sagemaker Alternatives
IBM Watson Studio
IBM Watson Studio builds, runs, and manages AI models on the IBM cloud. Working with frameworks like PyTorch, TensorFlow, and scikit-learn, as well as notebooks like JupyterLab and CLIs, this leading data science and machine learning solution helps enterprises accelerate model building and learning. While offering a free trial, there are multiple licensing options and pay-as-you-go pricing for any used model.
Vertex AI
Under their unified artificial intelligence platform, Google has built a cloud-based system where you can build, deploy, and even scale your machine learning models faster. The bonus is it’s developed by Google Research the frontrunner in data retrieval. Their tools require 80% fewer lines of code for custom modeling than other platforms. Their pay-as-you-go pricing follows the path of other platforms and you can get a cost estimate on the Vertex webpage to better gauge the costs for your business.
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Databricks vs Sagemaker: Final Verdict
Databricks offers more bang for your buck. According to consumer reviews, Sagemaker just doesn’t have the same power for large data models as Databricks. Databricks scores higher on usability, support, pricing, and professional services receiving an 8.8 out of 10 overall. Its ease of use and collaboration capabilities across cloud platforms makes it a great addition to your machine learning team.
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FAQ
What is a Data Lake?
Data at any scale can be stored in one centralized repository and this is called a Data Lake. You don’t have to run analytics on it first, Data Lakes stores structured and unstructured data.
Why do you need a data lake?
By successfully generating business value from your data, you will outperform your peers according to an Aberdeen Survey. According to this survey that business that utilized a data lake and implemented analytics from there, outperformed their competition by 9%.
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Why cloud computing?
Instead of buying and maintaining physical data centers and servers, which can be incredibly expensive, you can pay-as-you-go with cloud-based IT services like computing power, storage, and database access saving you money and time. Cloud computing serves any organization in many ways even outside data analytics and machine learning.
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