Databricks vs Jupyter

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Databricks

252
415
+ 1
8
Jupyter

1.1K
971
+ 1
44
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Pros of Databricks
Pros of Jupyter
  • 1
    Best Performances on large datasets
  • 1
    True lakehouse architecture
  • 1
    Scalability
  • 1
    Databricks doesn't get access to your data
  • 1
    Usage Based Billing
  • 1
    Security
  • 1
    Data stays in your cloud account
  • 1
    Multicloud
  • 15
    In-line code execution using blocks
  • 9
    In-line graphing support
  • 5
    Multiple kernel support
  • 5
    Can be themed
  • 3
    Best web-browser IDE for Python
  • 3
    Export to python code
  • 2
    LaTex Support
  • 1
    Multi-user with Kubernetes
  • 1
    HTML export capability

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What is Databricks?

Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications.

What is Jupyter?

The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media.

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What companies use Databricks?
What companies use Jupyter?
See which teams inside your own company are using Databricks or Jupyter.
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What tools integrate with Databricks?
What tools integrate with Jupyter?

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What are some alternatives to Databricks and Jupyter?
Snowflake
Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.
Azure Databricks
Accelerate big data analytics and artificial intelligence (AI) solutions with Azure Databricks, a fast, easy and collaborative Apache Spark–based analytics service.
Domino
Use our cloud-hosted infrastructure to securely run your code on powerful hardware with a single command — without any changes to your code. If you have your own infrastructure, our Enterprise offering provides powerful, easy-to-use cluster management functionality behind your firewall.
Confluent
It is a data streaming platform based on Apache Kafka: a full-scale streaming platform, capable of not only publish-and-subscribe, but also the storage and processing of data within the stream
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
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