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Category: Hadoop

Contrasting Kafka with Azure Service Bus

Published 2021-10-18 by Kevin Feasel

Ritam Das explains the differences between Apache Kafka and Azure Service Bus:

 It is important to note that Azure Service Bus is a traditional message broker and tailored to somewhat different use cases when compared to Kafka. Simply transferring between these two technologies is not an easy feat and would require overhauling your entire application. The comparison stops at both technologies being message brokers as under the hood they are fundamentally different. 

At a high level, ASB has high processing overhead per message, stronger guarantees around delivery and processing, and typically a “process once” model. Kafka has low overhead processing per message, fewer guarantees around delivery and processing, and typically a “publish once, process multiple times” model. To provide an explicit comparison, it would be best to understand the intended use case and proceed from there. 

Read on to understand the best uses for each technology, as well as sample calls using Python.

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Creating Delta Lake Tables in Azure Databricks

Published 2021-10-18 by Kevin Feasel

Gauri Mahajan takes us through creating new tables in a Delta Lake using Azure Databricks:

Delta lake is an open-source data format that provides ACID transactions, data reliability, query performance, data caching and indexing, and many other benefits. Delta lake can be thought of as an extension of existing data lakes and can be configured per the data requirements. Azure Databricks has a delta engine as one of the core components that facilitates delta lake format for data engineering and performance. Delta lake format is used to create modern data lake or lakehouse architectures. It is also used to build a combined streaming and batch architecture popularly known as lambda architecture.

Click through for the process.

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Architecting a Jenkins Replacement

Published 2021-10-18 by Kevin Feasel

Li Haoyi takes us through an internal Databricks tool for continuous integration:

Runbot is a bespoke continuous integration (CI) solution developed specifically for Databricks’ needs. Originally developed in 2019, Runbot incrementally replaces our aging Jenkins infrastructure with something more performant, scalable, and user friendly for both users and maintainers of the service. This blog post will explore the motivations behind developing Runbot, the core design decisions that went into it, and how we used it to greatly improve the experience of all the developers within the Databircks engineering organization.

It doesn’t look like the tool is available externally, but it’s an interesting read and helps understand some of the “why” behind the solution.

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Databricks Integration with Git Repos

Published 2021-10-11 by Kevin Feasel

Ka-Hing Chueng and Vaibhav Sethi announce Databricks Repos is now generally available:

Thousands of Databricks customers have adopted Databricks Repos since its public preview and have standardized on it for their development and production workflows. Today, we are happy to announce that Databricks Repos is now generally available.

Databricks Repos was created to solve a persistent problem for data teams: most tools used by data engineering/machine learning practitioners offer poor or no integration with Git version control systems, forcing them to navigate through multiple files, steps and UIs to simply review and commit code. Not only is this time-consuming, but it’s also error-prone.

This has been a bit of a pain point with Databricks in the past, and they’ve come up with this solution. Given that Azure Synapse Analytics has some of the same pain points, I’d expect we’ll see something similar in time.

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New in SQL Server Big Data Clusters

Published 2021-10-07 by Kevin Feasel

Daniel Coelho has an update on what’s available in SQL Server Big Data Clusters:

SQL Server Big Data Clusters (BDC) is a capability brought to market as part of the SQL Server 2019 release. Big Data Clusters extends SQL Server’s analytical capabilities beyond in-database processing of transactional and analytical workloads by uniting the SQL engine with Apache Spark and Apache Hadoop to create a single, secure, and unified data platform. It is available exclusively to run on Linux containers, orchestrated by Kubernetes, and can be deployed in multiple-cloud providers or on-premises.

Today, we’re proud to announce the release of the latest cumulative update, CU13, for SQL Server Big Data Clusters which includes important changes and capabilities:

Updating to the most recent production-ready version of Spark (as of today) is a nice upgrade.

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pyspark.pandas in Apache Spark 3.2

Published 2021-10-06 by Kevin Feasel

Hyukjin Kwon and Xinrong Meng announce a built-in pandas API for Apache Spark 3.2:

We’re thrilled to announce the pandas API as part of the upcoming Apache Spark™ 3.2 release. pandas is a powerful, flexible library and has grown rapidly to become one of the standard data science libraries. Now pandas users can leverage the pandas API on their existing Spark clusters.

A few years ago, we launched Koalas, an open source project that implements the pandas DataFrame API on top of Spark, which became widely adopted among data scientists. Recently, Koalas was officially merged into PySpark by SPIP: Support pandas API layer on PySpark as part of Project Zen (see also Project Zen: Making Data Science Easier in PySpark from Data + AI Summit 2021).

pandas users can now scale their workloads with one simple line change in the upcoming Spark 3.2 release:

Click through to see more details on the change.

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Apache Flink 1.14.0 Released

Published 2021-10-04 by Kevin Feasel

Stephan Ewen and Johannes Moser have aa round-up of the latest Apache Flink updates:

The Apache Software Foundation recently released its annual report and Apache Flink once again made it on the list of the top 5 most active projects! This remarkable activity also shows in the new 1.14.0 release. Once again, more than 200 contributors worked on over 1,000 issues. We are proud of how this community is consistently moving the project forward.

This release brings many new features and improvements in areas such as the SQL API, more connector support, checkpointing, and PyFlink. A major area of changes in this release is the integrated streaming & batch experience. We believe that, in practice, unbounded stream processing goes hand-in-hand with bounded- and batch processing tasks, because many use cases require processing historic data from various sources alongside streaming data. Examples are data exploration when developing new applications, bootstrapping state for new applications, training models to be applied in a streaming application, or re-processing data after fixes/upgrades.

Read on for the list of changes.

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Adaptive Query Execution in Spark 3

Published 2021-10-01 by Kevin Feasel

Amarjeet Singh explains what Adaptive Query Execution is in Apache Spark:

As we all know optimization plays an important role in the success of spark SQL. Therefore, a lot of work has been done in this direction. Before spark 3.0, cost-based optimization was a major hit in which different stages related to cost (based on time efficiency and estimated CPU and I/O usage) are compared and executes the strategy which minimizes the cost. But, because of outdated statistics, it has become a sub-optimal technique. Therefore in spark 3.0, Adaptive Query Execution was introduced which aims to solve this by reoptimizing and adjusts the query plans based on runtime statistics collected during query execution. Thus re-optimization of the execution plan occurs after every stage as each stage gives the best place to do the re-optimization.

Item number 2 from the list is also available in SQL Server, giving you an idea that this is an active battleground for query processing in data platform technologies.

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