Azure DataLake vs Data Bricks
What is Azure Data Lake?
A Data Lake is a collection of any and all data, acquired from a variety of sources. Data Lakes make it easy to store and analyze data to find new insights for business intelligence (BI). This can help your company make better business decisions by focusing on the needs of your customers.
Azure Data Lake is a cloud-based data storage service that enables data of any size, from gigabytes to petabytes. Use Azure Data Lake to store and manage big and complex data.
Azure Data Lake is a fully managed, petabyte-scale data storage and processing service for working with highly unstructured data. It allows you to store the raw data collected from various sources such as websites and sensors, do your work on that data without creating any ETL jobs, and then ingest the processed results back into Azure SQL Database or Azure Blob Storage.
How to use Azure Data Lake?
The following are the steps to use Azure Data Lake:
-Create an Azure Data Lake account.
-Register your data source in Data Lake Analytics.
-Create a data lake store and associate it with the registered data source.
-Create a file-based analytics job to run on your data lake store.
-Initiate the job using the Azure portal or a REST API call.
What is DataBricks?
DataBricks is a fully managed, petabyte-scale data lake as a service. It automatically analyzes and stores all your data in the cloud. Just point to where you store your data—from files and databases to APIs, IoT devices and sensors—and DataBricks will keep it safe, secure and searchable.
DataBricks is a managed database service for big data analytics that supports various technology stacks: Spark, Hadoop and R, SQL. It is a machine learning database that lets you create machine learning models from structured and unstructured data stored in any format.
DataBricks is a fully managed source of structured data, providing billions of rows in structured data. DataBricks includes the Apache Spark, Apache Tez and Apache Mesos software platforms.
DataBricks is a managed cloud analytics platform that enables you to perform complex data processing and analytics tasks while freeing you from the operational burden of managing infrastructure.
This can be done by invoking a series of user-defined functions (UDFs) defined in Apache Spark runtime, through which DataBricks takes care of the low level details such as resources provisioning, versioning, etc. It provides an intuitive interface to generate reliable UDF code that can be implemented quickly without any context switching
How to use Data Bricks?
1. Create a new project from the welcome page.
2. Select Apache Spark 2.0 and click on the ‘Create Project’ button.
3. Download the code and add it to your project by following these steps:
a) Click on the ‘Download Zip’ button on the welcome page.
b) Unzip the downloaded file (spark-data-crud-demo) and copy it to your project’s directory.
4. Update your project’s build.Gradle file to include the Data Bricks dependency.
5. Add the following lines to your build.Gradle file: dependencies { compile group: ‘com.data bricks, name: ‘data-crud’, version: ‘1.0.7’ }
Advantages of Azure Data Lake
1) Easy scalability and elasticity: You can easily scale up or down your data lake based on need.
2) High availability: The service has high availability which is guaranteed by using multiple data centers.
3) Full control: It is highly customizable from the UI dashboard.
4) Secure storage: You can have granular access controls for secure storage, lock collection, and backup systems for each table/file with user-defined policies
Disadvantages of Azure Data Lake
1) The service is not recommended for workloads that are sensitive to latency.
2) It does not support complex querying because it uses the same engine as the Azure SQL server and Data Factory.
3) You cannot use the service as a general-purpose database.
4) It is not suitable for transactional workloads because of its high latency and high availability requirements.
Advantages of Data Bricks
1) It is a cloud-native database service that can be used to build custom applications or store data.
2) It supports complex querying and also comes with a pre-built dashboard for monitoring, administration, and analytics.
3) You can use the service as a general-purpose database because it has high availability and supports transactional workloads.
4) It is suitable for OLTP and OLAP workloads because it has high availability and supports transactional workloads.
5) It runs on Linux containers, which makes it easy to deploy and manage.
6) You can use any SQL client or tool to connect to Dat Bricks because it is compatible with the Azure SQL server.
Disadvantages of Data Bricks
1) It is not free, but you can get a free trial and start with the smallest data size first.
2) The service has limited flexibility because it comes with pre-built dashboards for monitoring, administration, and analytics.
3) It might be hard to manage the cluster if you want to add more nodes or change the configuration.
4) It can only be used for one database, which means you cannot use it for multiple applications.
5) Data Bricks does not offer any managed services at the moment, but they are working on it.
6) The service does not support geo-replication at the moment.
Difference between Azure Data Lake vs Data Bricks
1) Data Lake is a fully managed service that allows you to store, process, and analyze large amounts of data at any scale.
2) It comes with pre-built dashboards for monitoring, administration, and analytics.
3) You can add more nodes or change the configuration if needed.
4) Azure Data Lake supports multiple databases, which means you can use it for multiple applications.
5) It offers a managed version called Azure Data Lake Storage Gen1 (ADLSG1).
6) It also provides geo-replication as well as data integration services like ETLs and APIs.
Databricks is a fully managed cloud service for data scientists, analysts, and developers.
1) It provides an IDE for writing code and running notebooks that can be shared with others.
2) You can run Spark jobs in Databricks on the same cluster where the notebook is running.
3) It can be used for batch and streaming data processing.
4) It provides a managed version called Databricks Delta, which allows you to store your data in S3, GCS, or Azure Blob Storage.
5) You can also use it with Amazon EMR or Azure HDInsight to run on-premises Hadoop clusters.
Azure DataLake vs Data Bricks
| Azure Data Lake | Data Bricks |
|---|---|
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Azure Data Lake is a service designed to help organizations manage and query massive datasets beyond the capabilities of the average database. |
Azure Databricks is an Apache Spark-powered cloud-hosted big data environment that provides interactive analytics in seconds on large data sets. |
|
Azure Data Lake is a service for storing and processing data of all sizes |
Azure Databricks is a managed service for interactive analytics on large datasets. |
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Azure Data Lake is a service that allows organizations to store and process large amounts of data, while Azure Databricks is a cloud-hosted big data environment. |
Azure Databricks offers interactive analytics on large datasets in seconds, while Azure Data Lake provides storage and processing capabilities for any size data set. |
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Azure Data Lake provides a fully managed, petabyte-scale repository and enables you to ingest data of all formats. |
Data Bricks, on the other hand, is a new fully managed service in Azure that enables you to build different components of your big data pipeline as independent modular units. |
Conclusion
In this blog post, you learned about Azure DataLake and Azure Data Bricks. You understand the higher level concepts of each service, but also learned how to build on top of both services to create your own analytics solution.
Azure DataLake vs Data Bricks is a recent trend in cloud data warehousing where many companies have started adopting these services, which provide greater flexibility in data storage and processing. Although both are designed to handle large amounts of data, they work differently and serve different purposes.
The fundamental difference is that Azure Data Lake Storage Gen2 and Azure Data Lake Store are separate services, while Data Bricks has deep integrations with Azure Blob Storage. Another important distinction is that Azure Data Lake Storage Gen2 and Azure Data Lake Store optimize data ingestion by reviewing the format of data going into them.
The focus of these products (and the related services) is to optimize data processing rather than to simply ingest data into an object store.
Data Lake is the next step in a more scalable, cost-effective analytics. One of the benefits of moving from SQL Server to Data Lake is that it enables users to get full analytical insight at all stages of their workload without having to worry about performance bottlenecks and other connectivity issues.