We researched AWS SageMaker, but in the end, we chose Databricks.
Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It is ideal for big data projects, especially cloud-based ones. The software runs Spark in the background, so this translates into simpler operations and reduced costs. We use it for data warehousing, real-time monitoring, and data governance. It features SQL, is very user-friendly, and is very adaptable for a variety of use cases. You can also use it for data engineering, machine learning, AI, and other data science projects.
It is great for scheduled and ad hoc jobs, too. In summary, it allows you the opportunity to enjoy a ready-to-use Spark environment without having to configure it. It also supports multiple languages, like Python, Java, and R.
The most critical downside is that Databricks doesn’t have a data backup feature. It gets tricky with load times, which are quite inconsistent. Another thing they could improve is the lack of explanation in error messages.
We looked into AWS SageMaker, and it is a solid option for teams working more with machine learning and machine learning operations. It supports Jupyter notebooks and multiple languages and libraries. The system is cloud-based, and they have a pay-as-you-go pricing model,
One advantage of SageMaker is that you can choose multiple servers to train your ML models, and all data and projects are stored in S3. But it is hard for a new data scientist or someone without strong programming expertise. Also, if you need AWS SageMaker for other models that are not ML, you’ll have difficulty integrating them. Finally, we find it takes too long to run large data sets.
Conclusions
While AWS SageMaker is improving, the slow pace for big data sets made it impractical for us. We prefer Databricks.
Databricks and Amazon SageMaker both compete in the machine learning platforms category. Based on feature integration, Databricks seems to have an advantage with its ease of use and collaborative capabilities, whereas Amazon SageMaker excels in deployment flexibility and AWS integration.Features: Databricks integrates multiple programming languages like SQL, Python, and Spark, facilitating robust machine learning workflows. It offers collaborative notebooks and seamless scalability, enhancing...
We researched AWS SageMaker, but in the end, we chose Databricks.
Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It is ideal for big data projects, especially cloud-based ones. The software runs Spark in the background, so this translates into simpler operations and reduced costs. We use it for data warehousing, real-time monitoring, and data governance. It features SQL, is very user-friendly, and is very adaptable for a variety of use cases. You can also use it for data engineering, machine learning, AI, and other data science projects.
It is great for scheduled and ad hoc jobs, too. In summary, it allows you the opportunity to enjoy a ready-to-use Spark environment without having to configure it. It also supports multiple languages, like Python, Java, and R.
The most critical downside is that Databricks doesn’t have a data backup feature. It gets tricky with load times, which are quite inconsistent. Another thing they could improve is the lack of explanation in error messages.
We looked into AWS SageMaker, and it is a solid option for teams working more with machine learning and machine learning operations. It supports Jupyter notebooks and multiple languages and libraries. The system is cloud-based, and they have a pay-as-you-go pricing model,
One advantage of SageMaker is that you can choose multiple servers to train your ML models, and all data and projects are stored in S3. But it is hard for a new data scientist or someone without strong programming expertise. Also, if you need AWS SageMaker for other models that are not ML, you’ll have difficulty integrating them. Finally, we find it takes too long to run large data sets.
Conclusions
While AWS SageMaker is improving, the slow pace for big data sets made it impractical for us. We prefer Databricks.