I am currently working on a small team that is developing a Databricks based solution. For now we are small enough to work off of cloud instances of Databricks. As the group grows this will not really be practical.
Is there a "local" install of Databricks that can be installed for development purposes (it doesn't need to be a scalable version but does need to be essentially fully featured)? In other words, is there a way each developer can create their own development instance of Databricks on their local machine?
Is there another way to provide a dedicated Databricks environment for each developer?
Databricks, as a cloud-deployed platform, leverages many cloud technologies in its deployment. For example, Auto Loader incrementally ingests new data files as they arrive in AWS using EventBridge, SNS and S3, while Azure uses EventHubs, Notification Hubs and ADLS technologies. They aim to create a seamless look and feel across AWS, Azure and GCP but can do this only in the cloud.
For local deployment, you may be able to use Apache Spark and MlFlow and create a similar experience, but the notebook experience isn't open source. The workflow of Databricks is proprietary, though Databricks has open-sourced many of its technologies, like Delta Lake. The local Spark, MlFlow, may suffice for some and then use the cloud sparingly, but the seamless workflow offered by Databricks is challenging to replicate outside of the leading cloud vendors.
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How can I process the given data efficiently using Apache Spark on a cloud Infrastructure as a Service (IaaS) platform? I have a dataset of over 60 million data that I need to run the dataset effectively.
There are many options to do the same.
In Azure you can use Synapse/Azure Data Factory.
In GCS,you can use Dataproc cluster with Cloud Composer.It would be great if you can mention the whole scenario what is your exact source(csv/RDBMS table/IOT) and what would be the target/sink then it would be easier to provide answer
When creating a Spark cluster within an Azure Synapse workspace, is there a means to install arbitrary files and directories onto it's cluster nodes and/or onto the node's underlying distributed filesystem?
By arbitrary files and directories, I literally mean arbitrary files and directories; not just extra Python libraries like demonstrated here.
Databricks smartly provided a means to do this on it's cluster nodes (described in this document). Now I'm trying to see if there's a means to do the same on an Azure Synapse Workspace Spark Cluster.
Thank you.
Unfortunately, Azure Synapse Analytics don't support arbitrary binary installs or writing to Spark local storage.
I would suggest you to provide feedback on the same:
https://feedback.azure.com/forums/307516-azure-synapse-analytics
All of the feedback you share in these forums will be monitored and reviewed by the Microsoft engineering teams responsible for building Azure.
I'm very new to the Azure Data Lake Storage and currently training on Data Factory. I have a developer background so right away I'm not a fan of the 'tools' approach for development. I really don't like how there's all these settings to set and objects you have to create everywhere. I much prefer a code approach which allows us to detach the logic from the service (don't like the publishing thing to save), see everything by scrolling or navigate to different objects in a project, see differences easier in source control and etc. So I found this Micrososft's Filesystem SDK that seems to be an alternative to Data Factory:
https://azure.microsoft.com/en-us/blog/filesystem-sdks-for-azure-data-lake-storage-gen2-now-generally-available/
What has been your experience using this approach? Is this a good alternative? Is there a way to run SDK code in data factory? that way we can leverage scheduling and triggers? I guess i'm looking for Pros/cons.
thank you
Well, the docs refer to several SDKs, one of them being the .Net SDK and the title is
Use .NET (or Python or Java etc.) to manage directories, files, and ACLs in Azure Data Lake Storage Gen2
So, the SDK lets you manage the filesystem only. No support for triggers, pipelines, dataflows and the lot. You will have to stick to the Azure Data Factory for that.
Regarding this:
I'm not a fan of the 'tools' approach for development
I hate to tell you but the world is moving that way whether you like it or not. Take Logic Apps for example. Azure Data Factory isn't aimed at the hardcore developer but fulfils a need for people working with large sets of data like Data Engineers. I am already glad it integrates with git very well. Yes, there is some overhead in defining sinks and sources but they are reusable across pipelines.
If you really want to use code try Azure Databricks. Take a look at this Q&A as well.
TL;DR:
The FileSystem SDK is not an alternative.
The code-centric alternative to Azure Data Factory for building and managing your Azure Data Lake is Spark. Typically either Azure Databricks or Azure Synapse Spark.
In the past, the following project structure has been useful in etl development using pyspark (on-premises infrastructure)
Since Azure Databricks uses the concept of Notebooks, I'm not certain how this structure needs to be modified to be able to deploy etl pipelines using Azure Databricks.
Can someone please share what'd be an equivalent representation of this structure in an Azure Databricks workspace?a
I have a requirement to parse a lot of small files and load them into a database in a flattened structure. I prefer to use ADF V2 and SQL Database to accomplish it. The file parsing logic is already available using Python script and I wanted to orchestrate it in ADF. I could see an option of using Python Notebook connector to Azure Databricks in ADF v2. May I ask if I will be able to just run a plain Python script in Azure Databricks through ADF? If I do so, will I just run the script in Databricks cluster's driver only and might not utilize the cluster's full capacity. I am also thinking of calling Azure functions as well. Please advise which one is more appropriate in this case.
Just provide some ideas for your reference.
Firstly, you are talking about Notebook and Databricks which means ADF's own copy activity and Data Flow can't meet your needs, since as i know, ADF could meet just simple flatten feature! If you miss that,please try that first.
Secondly,if you do have more requirements beyond ADF features, why not just leave it?Because Notebook and Databricks don't have to be used with ADF,why you want to pay more cost then? For Notebook, you have to install packages by yourself,such as pysql or pyodbc. For Azure Databricks,you could mount azure blob storage and access those files as File System.In addition,i suppose you don't need many workers for cluster,so just configure it as 2 for max.
Databricks is more suitable for managing as a job i think.
Azure Function also could be an option.You could create a blob trigger and load the files into one container. Surely,you have to learn the basic of azure function if you are not familiar with it.However,Azure Function could be more economical.