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Category: Spark

Spark Performance Improvements in Azure Synapse

Published 2021-09-22 by Kevin Feasel

Balaji Sankaran shows improvements Microsoft has made over open-source Apache Spark 3 in Azure Synapse Analytics:

Azure Synapse Analytics is continually focused on delivering a highly performant and scalable platform for supporting Spark Workload. We are focused on improving the query performance for the typical workload patterns that we see with our customers. By combining the latest open-source updates in Apache Spark with our team’s focus on performance updates we have made significant performance gains in standard TPC-DS benchmarking tests.

I expect it will never be as fast as what Databricks can do, but getting a 2x performance improvement over the open source version of Spark is nothing to sneeze at.

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Type 1 SCDs in Delta Lake

Published 2021-09-16 by Kevin Feasel

Chris Williams starts a series on slowly changing dimensions in a Delta Lake:

Anyone that has contributed towards a Data Warehouse or a dimensional model in Power BI will know the distinction made between the time-series metrics of a Fact Table and the categorised attributes of a Dimension Table. These dimensions are also affected by the passage of time and require revised descriptions periodically which is why they are known as Slowly Changing Dimensions (SCD). See The Data Warehouse Toolkit – Kimball & Ross for more information.

Here is where the Delta Lake comes in. Using its many features such as support for ACID transactions (Atomicity, Consistency, Isolation and Durability) and schema enforcement we can create the same durable SCD’s. This may have required a series of complicated SQL statements in the past to achieve this. I will now discuss a few of the most common SCD’s and show how they can be easily achieved using a few Databricks Notebooks, which are available from my GitHub repo so you can download and have a go:

https://github.com/cwilliams87/Blog-SCDs

Check out the repo, but be sure to read the whole post.

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Data Lakehouse Point-of-Sale Analytics Demo

Published 2021-09-10 by Kevin Feasel

Bryan Smith and Rob Saker share a pattern:

Disruptions in the supply chain – from reduced product supply and diminished warehouse capacity – coupled with rapidly shifting consumer expectations for seamless omnichannel experiences are driving retailers to rethink how they use data to manage their operations. Prior to the pandemic, 71% of retailers named lack of real-time visibility into inventory as a top obstacle to achieving their omnichannel goals. The pandemic only increased demand for integrated online and in-store experiences, placing even more pressure on retailers to present accurate product availability and manage order changes on the fly. Better access to real-time information is the key to meeting consumer demands in the new normal.

In this blog, we’ll address the need for real-time data in retail, and how to overcome the challenges of moving real-time streaming of point-of-sale data at scale with a data lakehouse.

It’s a cool scenario, at the least.

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Databricks Serverless SQL

Published 2021-09-09 by Kevin Feasel

Nikhil Jethava and Kevin Clugage announce serverless SQL on Databricks:

Databricks SQL already provides a first-class user experience for BI and SQL directly on the data lake, and today, we are excited to announce another step in making data and AI simple with Databricks Serverless SQL. This new capability for Databricks SQL provides instant compute to users for their BI and SQL workloads, with minimal management required and capacity optimizations that can lower overall cost by an average of 40%. This makes it even easier for organizations to expand adoption of the lakehouse for business analysts who are looking to access the rich, real-time datasets of the lakehouse with a simple and performant solution.

Under the hood of this capability is an active server fleet, fully managed by Databricks, that can transfer compute capacity to user queries, typically in about 15 seconds. The best part? You only pay for Serverless SQL when users start running reports or queries.

Things are getting interesting between Databricks and Azure Synapse Analytics, as both now have serverless SQL and Spark offerings. Synapse Analytics has the better implementation for serverless SQL and Databricks the superior Spark implementation, so it becomes a question of which weakness you take in order to gain the strength.

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Databricks Autologging

Published 2021-08-31 by Kevin Feasel

Corey Zumar and Kasey Uhlenhuth announce a new product:

Machine learning teams require the ability to reproduce and explain their results–whether for regulatory, debugging or other purposes. This means every production model must have a record of its lineage and performance characteristics. While some ML practitioners diligently version their source code, hyperparameters and performance metrics, others find it cumbersome or distracting from their rapid prototyping. As a result, data teams encounter three primary challenges when recording this information: (1) standardizing machine learning artifacts tracked across ML teams, (2) ensuring reproducibility and auditability across a diverse set of ML problems and (3) maintaining readable code across many logging calls.

Read on to see how Databricks Autologging can satisfy these issues.

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Generating Artificial Data with Databricks Generator

Published 2021-08-13 by Kevin Feasel

Ust Oldfield shows off a new tool:

Databricks Labs is a relatively new offering from Databricks which showcases what their teams have been creating in the field to help their customers. As a Consultant, this makes my life a lot easier as I don’t have to re-invent the wheel and I can use it to demonstrate value in partnering with Databricks. There’s plenty of use cases that I’ll be using, and extending, with my client but the one I want to focus on in this post is the Data Generator.

Read on for an example of how this works. Something not in Ust’s post but worth mentioning is that you can control the distribution of random numeric features. That’s a piece of functionality you often don’t see in data generators.

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How Spark Determines Task Numbers and Parallelism

Published 2021-08-06 by Kevin Feasel

The Hadoop in Real World team explains how the Spark engine decides how many tasks to create for a job and how many can run in parallel:

In this post we will see how Spark decides the number of tasks and number of tasks to execute in parallel in a job.

Let’s see how Spark decides on the number of tasks with the below set of instructions.

[… instructions]

Let’s also assume dataset_Y has 10 partitions and dataset_Y has 5 partitions.

Click through for the full explanation.

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Contrasting Scala and Python wrt Spark

Published 2021-07-20 by Kevin Feasel

Sanjay Rathore contrasts two of the three key Apache Spark languages:

Imagine the first day of a new Apache Spark project. The project manager looks at the team and says: which one to choose, scala or python. So let’s start with “scala vs python for spark”. 

You may wonder if this is a tricky question. What does the enterprise demand say? Is this like asking iOS or Android? Is there a right or wrong answer?

So we are here to inform and provide clarity. Today we’re looking at two popular programming languages, Scala and Python, and comparing them in the context of Apache Spark and Big Data in general.

Read on for the comparison. I’m at a point where I think it’s wise to know both languages and roll with whichever is there. If you’re in a greenfield Spark implementation, pick the one you (or your team) is more comfortable with. If you’re equally comfortable with the two, pick Scala because it’s a functional programming language and those are neat.

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