Press "Enter" to skip to content

Category: Data Lake

Architecting a Data Lake in AWS

Published 2019-10-08 by Kevin Feasel

Gaurav Mishra takes us through data lake architecture on AWS:

Landing zone: This is the area where all the raw data comes in, from all the different sources within the enterprise. The zone is strictly meant for data ingestion, and no modelling or extraction should be done at this stage.

Curation zone: Here’s where you get to play with the data. The entire extract-transform-load (ETL) process takes place at this stage, where the data is crawled to understand what it is and how it might be useful. The creation of metadata, or applying different modelling techniques to it to find potential uses, is all done here.

Production zone: This is where your data is ready to be consumed into different application, or to be accessed by different personas. 

This is a nice overview of data lake concepts and worth the read if you’re using AWS. Even if not, the same principles (if not the same technologies) apply for Azure, other clouds, and on-prem.

Comments closed

From Data Lake to Delta Lake

Published 2019-10-08 by Kevin Feasel

Anmol Sarna explains the benefits of Spark’s Delta Lake:

Traditionally data has been residing in silos across the organization and the ecosystem in which it operations (external data). That’s a challenge: you can’t combine the right data to succeed in a big data project if that data is a bit everywhere in and out of the cloud. This is where the idea – and reality – of (big) data lakes comes from.

A data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed. While a hierarchical data warehouse stores data in files or folders, a data lake uses a flat architecture to store data.

Although the data lakes serve as a central ingestion point for the plethora of data that organizations seek to gather and mine, it still has various limitations or challenges.

Software is made up of tradeoffs. What you gain by dumping relational structure, you lose in dumping relational structure (and this applies in the opposite direction as well).

Comments closed

Accessing Data in Azure Data Lake Storage Gen 2

Published 2019-09-18 by Kevin Feasel

James Serra gives us several methods to access data in Azure Data Lake Storage Gen 2:

With data lakes becoming popular, and Azure Data Lake Store (ADLS) Gen2 being used for many of them, a common question I am asked about is “How can I access data in ADLS Gen2 instead of a copy of the data in another product (i.e. Azure SQL Data Warehouse)?”. The benefits of accessing ADLS Gen2 directly is less ETL, less cost, to see if the data in the data lake has value before making it part of ETL, for a one-time report, for a data scientist who wants to use the data to train a model, or for using a compute solution that points to ADLS Gen2 to clean your data. While these are all valid reasons, you still want to have a relational database (see Is the traditional data warehouse dead?). The trade-off in accessing data directly in ADLS Gen2 is slower performance, limited concurrency, limited data security (no row-level, column-level, dynamic data masking, etc) and the difficulty in accessing it compared to accessing a relational database.

Since ADLS Gen2 is just storage, you need other technologies to copy data to it or to read data in it.

Read on for the solution.

Comments closed

Machine Learning and Delta Lake

Published 2019-08-19 by Kevin Feasel

Brenner Heintz and Denny Lee walk us through solving data engineering problems with Delta Lake:

As a result, companies tend to have a lot of raw, unstructured data that they’ve collected from various sources sitting stagnant in data lakes. Without a way to reliably combine historical data with real-time streaming data, and add structure to the data so that it can be fed into machine learning models, these data lakes can quickly become convoluted, unorganized messes that have given rise to the term “data swamps.”

Before a single data point has been transformed or analyzed, data engineers have already run into their first dilemma: how to bring together processing of historical (“batch”) data, and real-time streaming data. Traditionally, one might use a lambda architecture to bridge this gap, but that presents problems of its own stemming from lambda’s complexity, as well as its tendency to cause data loss or corruption.

Read the whole thing.

Comments closed

Data Cleansing Options with Azure

Published 2019-05-03 by Kevin Feasel

James Serra tries to give you an answer of when you should use different Azure services for data cleansing:

Clean the data and optionally aggregate it as it sits in source system.  The tool used for this would depend on the source system that stores the data (i.e. if SQL Server, you would use stored procedures).  The only benefit with this option is if you aggregate the data, you will move less data from the source system to Azure, which can be helpful if you have a small pipe to Azure and don’t need the row-level details.  The disadvantages are: the raw source data is not available in the data lake, so you would always need to go back to source system if you needed to get it again, and it may not even still exist in the source system; you would put extra stress on the source system when doing the cleaning which could affect end users using the system; it could take a long time to clean the data as the source system may not have fast performance; and you would not be able to use other tools (i.e. Hadoop, Databricks) to clean it.  Strongly advise against this option

Read on for additional options and James’s recommendations.

Comments closed

Azure Blob Storage and Data Lake Storage Gen2

Published 2019-03-11 by Kevin Feasel

Melissa Coates shows what you need to know about Azure Blob Storage with Azure Data Lake Storage Gen2:

– You may need to consider separate storage accounts if you need to segregate access control (RBAC), virtual networks, access keys, and the like. (Note that RBAC can also be set at the container level too, but ACL type permissions only apply to ADLS Gen2 and not to blob storage.)
– If you don’t need the hierarchical namespace whatsoever (for non-analytical use cases), this could mean a separate storage account. The storage cost is the same but transaction costs are higher when the HNS is enabled (discussed in item #8 of this post).

Click through for more details, including several more tips about Azure Storage Accounts, Azure Blob Storage Containers, and the Azure Storage Blobs themselves.

Comments closed

Learning More About Azure Data Lake Storage Gen2

Published 2019-02-27 by Kevin Feasel

Melissa Coates shares a compendium of links pertaining to Azure Data Lake Storage Generation 2:

A couple of people have asked me recently about how to ‘bone up’ on the new data lake service in Azure. The way I see it, there are two aspects: A, the technology itself and B, data lake principles and architectural best practices. Below are some links to resources that you should find helpful.

There’s a lot of good stuff there.

Comments closed

Azure Data Lake Store Gen2

Published 2019-02-22 by Kevin Feasel

James Serra gives us the low-down on Azure Data Lake Store Gen2 now that it is generally available:

When to use Blob vs ADLS Gen2
New analytics projects should use ADLS Gen2, and current Blob storage should be converted to ADLS Gen2, unless these are non-analytical use cases that only need object storage rather than hierarchical storage (i.e. video, images, backup files), in which case you can use Blob Storage and save a bit of money on transaction costs (storage costs will be the same between Blob and ADLS Gen2 but transaction costs will be a bit higher for ADLS Gen2 due to the overhead of namespaces).

Looks like there are still some things missing from Gen2, so don’t automatically jump on an upgrade. Read the documentation first to make sure you aren’t relying on something which isn’t there yet.

Comments closed

Data Lake Organization Tips

Published 2019-01-21 by Kevin Feasel

Melissa Coates has some great advice for people working with data lakes:

Q: Partitioning by date is common. Where should the dates go in the folder hierarchy?

Almost always, you will want the dates to be at the end of the folder path. This is because we often need to set security at specific folder levels (such as by subject area), but we rarely set up security based on time elements.

Optimal for folder security: \SubjectArea\DataSource\YYYY\MM\DD\FileData_YYYY_MM_DD.csv

Tedious for folder security: \YYYY\MM\DD\SubjectArea\DataSource\FileData_YYYY_MM_DD.csv

Click through for all of Melissa’s advice in FAQ form.

Comments closed

Choosing Azure Data Lake Analytics Versus Azure Databricks

Published 2019-01-02 by Kevin Feasel

Ginger Grant helps us make the decision between using Azure Data Lake Analytics and Azure Databricks:

Databricks is a recent addition to Azure that is greatly influencing the technology choices that people are making when determining how to process data.  Prior to the introduction of Databricks to Azure in March of 2018, if you had a lot of unstructured data which was stored in HDFS clusters, and wanted to analyze it in a scalable fashion, the choice was Data Lake and using USQL with Data Lake Analytics.  With the introduction of Databricks, there is now a choice for analysis between Data Lake Analytics and Databricks for analyzing data.

Click through for the comparison.

Comments closed