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Azure Databricks vs Databricks: What are the differences?
What is Azure Databricks? Fast, easy, and collaborative Apache Spark–based analytics service. Accelerate big data analytics and artificial intelligence (AI) solutions with Azure Databricks, a fast, easy and collaborative Apache Spark–based analytics service.
What is Databricks? A unified analytics platform, powered by Apache Spark. 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.
Azure Databricks and Databricks can be categorized as "General Analytics" tools.
Some of the features offered by Azure Databricks are:
- Optimized Apache Spark environment
- Autoscale and auto terminate
- Collaborative workspace
On the other hand, Databricks provides the following key features:
- Built on Apache Spark and optimized for performance
- Reliable and Performant Data Lakes
- Interactive Data Science and Collaboration
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Learn MorePros of Azure Databricks
Pros of Databricks
Pros of Azure Databricks
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Pros of Databricks
- Best Performances on large datasets1
- True lakehouse architecture1
- Scalability1
- Databricks doesn't get access to your data1
- Usage Based Billing1
- Security1
- Data stays in your cloud account1
- Multicloud1
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What is Azure Databricks?
Accelerate big data analytics and artificial intelligence (AI) solutions with Azure Databricks, a fast, easy and collaborative Apache Spark–based analytics service.
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.
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What tools integrate with Azure Databricks?
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What are some alternatives to Azure Databricks and Databricks?
Azure Machine Learning
Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning.
Azure HDInsight
It is a cloud-based service from Microsoft for big data analytics that helps organizations process large amounts of streaming or historical data.
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.
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 Data Factory
It is a service designed to allow developers to integrate disparate data sources. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud.