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Category: Data Lake

Data Lake Zoning

Published 2017-04-26 by Kevin Feasel

Parth Patel, et al, explain that there ought to be several zones of data within a data lake:

Within a Data Lake, zones allow the logical and/or physical separation of data that keeps the environment secure, organized, and Agile. Typically, the use of 3 or 4 zones is encouraged, but fewer or more may be leveraged. A generic 4-zone system might include the following:

  1. Transient Zone — Used to hold ephemeral data, such as temporary copies, streaming spools, or other short-lived data before being ingested.
  2. Raw Zone – The zone in which raw data will be maintained. This is also the zone where sensitive data must be encrypted, tokenized, or otherwise secured.
  3. Trusted Zone – After Data Quality, Validation, or other processing is performed on data in the Raw Zone, it becomes the “source of truth” in this zone for downstream systems.
  4. Refined Zone – Manipulated and enriched data is kept in this zone. This is used to store the output from tools like Hive or external tools that will write into to the Data Lake.

Your particular situation may differ but I’d consider this to be good advice no matter where or how you’re storing data, such as a classical data warehouse or an ODS.

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Azure Data Lake Store Best Practices

Published 2017-04-11 by Kevin Feasel

Ust Oldfield provides recommendations on how to size and lay out files in Azure Data Lake Store:

The format of the file has a huge implication for the storage and parallelisation. Splittable formats – files which are row oriented, such as CSV – are parallelizable as data does not span extents. Non-splittable formats, however, – files what are not row oriented and data is often delivered in blocks, such as XML or JSON – cannot be parallelized as data spans extents and can only be processed by a single vertex.

In addition to the storage of unstructured data, Azure Data Lake Store also stores structured data in the form of row-oriented, distributed clustered index storage, which can also be partitioned. The data itself is held within the “Catalog” folder of the data lake store, but the metadata is contained in the data lake analytics. For many, working with the structured data in the data lake is very similar to working with SQL databases.

This is the type of thing that you can easily forget about, but it makes a huge difference down the line.

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Biml And ADF, Part 2

Published 2017-03-13 by Kevin Feasel

Meagan Longoria builds Azure Data Factory pipelines using BimlScript:

The great thing about Biml is that I can use it as much or as little as I feel is helpful. That T-SQL statement to get column lists could have been Biml, but it didn’t have to be. The client can maintain and enhance these pipelines with or without Biml as they see fit. There is no vendor lock-in here. Just as with Biml-generated SSIS projects, there is no difference between a hand-written ADF solution and a Biml-generated ADF solution, other than the Biml-generated solution is probably more consistent.

And have I mentioned the time savings? There is a reason why Varigence gives out shirts that say “It’s Monday and I’m done for the week.”

Click through for the script.

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U-SQL Custom Python Libraries

Published 2017-03-13 by Kevin Feasel

Saveen Reddy explains how to build a custom Python library and use it with U-SQL:

First, let’s talk about “zipimport”. Thanks to the adoption of PEP 273 – Python had the ability to import modules from ZIP files since Python 2.3. This ability is called “zipimport” and is a built-in feature of the Python’s existing import statement. Read the zipimport documentation now.

To review the basics.

  • You create a module (a .py file, etc.)

  • ZIP up the module into a .zip file

  • Add the path to the .zip file to sys.path

  • Then import the module

Read on for the step-by-step process.

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Data Lake 3.0

Published 2017-03-07 by Kevin Feasel

Vinod Kumar Vavilapalli describes the modern data lake:

During the past few years though, end-to-end business use-cases have evolved to another level.

  • The end-to-end business problems are now mostly solved by multiple applications working together.
  • As the platform matured, users have increasingly started wanting to solely focus on the business application layers, and getting impatient to get on with developing their main business-logic.
  • However, YARN, and for that matter any other related platform, hasn’t catered to this evolving need, leaving the users to unwillingly get involved in the painstaking details of wiring applications together, keeping them up, manually scaling them as need arises etc.

Manual plumbing of all these different colored services in tiresome! Further, there is a clear need for seamless aggregate deployment, lifecycle management and application wireup. This is the gap that needs to be bridged between what these end-to-end business use-cases need from the platform and what the platform offers today. If these features are provided, then the business use cases authors can singularly focus on the business logic.

This is a higher-level “where are we at?” kind of post which could be helpful if you’re new to the data lake concept.

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Using Azure Data Factory With Biml

Published 2017-03-06 by Kevin Feasel

Meagan Longoria has a multi-part series on using Biml to script Azure Data Factory tasks to migrate data from an on-prem SQL Server instance to Azure Data Lake Store.  Here’s part 1:

My Azure Data Factory is made up of the following components:

  • Gateway – Allows ADF to retrieve data from an on premises data source

  • Linked Services – define the connection string and other connection properties for each source and destination

  • Datasets – Define a pointer to the data you want to process, sometimes defining the schema of the input and output data

  • Pipelines – combine the data sets and activities and define an execution schedule

Click through for the Biml.

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Using Azure Data Lake Store With Hadoop

Published 2017-02-22 by Kevin Feasel

Amit Kulkarni shows how to make Azure Data Lake Store the default file system for a Hadoop cluster:

So to give a concrete example, if the default file system was hdfs://123.23.12.4344:9000 then the /user/filename.txt would resolve to hdfs://123.23.12.4344:9000/user/filename.txt.

Why does the default file system matter? The first answer to this is purely convenience. It is a heck lot easier to simply say /events/sensor1/ than adl://amitadls.azuredatalakestore.net/ in code and configurations. Secondly, many components in Hadoop use relative paths by default. For instance there are a fixed set of places, specified by relative paths, where various applications generate their log files. Finally, many ISV applications running on Hadoop specify important locations by relative paths.

Read on to see how.

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Integrating Data Lake Storage With SQL Data Warehouse

Published 2017-02-07 by Kevin Feasel

Sachin Sheth alerts us to a new integration point between Azure Data Lake Storage and Azure SQL Data Warehouse via Polybase:

Most common patterns using Azure Data Lake Store (ADLS) involve customers ingesting and storing raw data into ADLS. This data is then cooked and prepared by analytic workloads like Azure Data Lake Analytics and HDInsight. Once cooked this data is then explored using engines like Azure SQL Data Warehouse. One key pain point for customers is having to wait for a substantial time after the data was cooked to be able to explore it and gather insights. This was because the data stored in ADLS would have to be loaded into SQL Data Warehouse using tools row-by-row insertion. But now, you don’t have to wait that long anymore. With the new SQL Data Warehouse PolyBase support for ADLS, you will now be able to load and access the cooked data rapidly and lessen your time to start performing interactive analytics. PolyBase support will allow to you access unstructured/semi-structured files in ADLS faster because of a highly scalable loading design. You can load the files stored in ADLS into SQL Data Warehouse to perform analytics with fast response times or you use can the files in ADLS as external tables. So get ready to unlock the value stored in your petabytes of data stored in ADLS.

I’ve been waiting for this support, and I’m happy that they were able to integrate the two products.

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Local Azure Data Lake

Published 2017-02-07 by Kevin Feasel

Julie Koesmarno shows how to set up Azure Data Lake for local testing:

Late last year, I presented a Cognitive Intelligence demo using Azure Data Lake (ADL) at PASS Summit keynote. It was a fun and quick demo! Watch it here :)

In case you’re new to ADL, you can now (since Dec 2015) develop, compile and run ADL locally in Visual Studio. This is huge! Because you don’t have to worry about your ADL Analytics Unit (AU) consumptions. Plus, this allows you to try it before you buy it too!

Click through for the step-by-step installation instructions.

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