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Category: Hadoop

Key Constraints in Databricks Unity Catalog

Meagan Longoria gives us a warning:

I’ve been building lakehouses using Databricks Unity catalog for a couple of clients. Overall, I like the technology, but there are a few things to get used to. This includes the fact that primary key and foreign key constraints are informational only and not enforced.

If you come from a relational database background, this unenforced constraint may bother you a bit as you may be used to enforcing it to help with referential integrity. 

Read on to see what is available and why it can nonetheless be useful in some circumstances.

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Lakehouse Management in Fabric via mssparkutils

Sandeep Pawar scripts out some lakehouse work:

At MS Ignite, Microsoft unveiled a variety of new APIs designed for working with Fabric items, such as workspaces, Spark jobs, lakehouses, warehouses, ML items, and more. You can find detailed information about these APIs here. These APIs will be critical in the automation and CI/CD of Fabric workloads.

With the release of these APIs, a new method has been added to the mssparkutils library to simplify working with lakehouses. In this blog, I will explore the available options and provide examples. Please note that at the time of writing this blog, the information has not been published on the official documentation page, so keep an eye on the documentation for changes.

This looks to be quite useful for CI/CD work.

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An Overview of Data Lake Operations with Apache NiFi

Lav Kumar gives us a 10,000 foot view:

In the world of data-driven decision-making, ETL (Extract, Transform, Load) processes play a pivotal role. The effective management and transformation of data are essential to ensure that businesses can make informed choices based on accurate and relevant information. Data lakes have emerged as a powerful way to store and analyze massive amounts of data, and Apache NiFi is a robust tool for streamlining ETL processes in a data lake environment.

Read on for a brief primer on NiFi and how some of its capabilities can assist in ETL and ELT processing.

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Apache Zookeeper Vulnerability

The Instaclustr team reviews an announcement:

On October 11, 2023, the Apache ZooKeeper™ project announced that a security vulnerability has been identified in Apache ZooKeeper, CVE-2023-44981. The Apache ZooKeeper project has classified the severity of this CVE as critical. The CVSS (Common Vulnerability Scoring System) 3.x severity rating for this vulnerability by the NVD (National Vulnerability Database) is base score 9.1 Critical.  

That’s a rather high base score and is comes about if you have the setting quorum.auth.enableSasl=true. Updating to the Zookeeper 3.7.2 or alter, 3.8.3 or later, or anything in the 3.9 branch will fix this vulnerability.

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Capturing a TCP Dump in an Azure Databricks Notebook

Stithi Panigrahi does some troubleshooting:

Due to the potential impact on performance and storage costs, Azure Databricks clusters don’t capture networking logs by default. Follow the below instructions if you need to capture tcpdump to investigate multiple networking issues related to the cluster. These steps will capture a TCP dump on each cluster node–both driver and workers during the entire lifetime of the cluster.

Click through for an initiation script, which generates the actual script, which itself generates the TCP dumps.

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Using Data Contracts in Confluent Schema Registry

Robert Yokota shows us how to generate data contracts for streaming solutions:

A data contract is a formal agreement between an upstream component and a downstream component on the structure and semantics of data that is in motion. The upstream component enforces the data contract, while the downstream component can assume that the data it receives conforms to the data contract. Data contracts are important because they provide transparency over dependencies and data usage in a streaming architecture. They help to ensure the consistency, reliability, and quality of the data in event streams, and they provide a single source of truth for understanding the data in motion.

Click through for a sample application that uses data contracts.

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Running Apache Flink Jobs from HDInsight

Sairam Yeturi builds a streaming job:

Could you already complete creating your first Apache Flink® cluster and submit your streaming job on it with HDInsight on AKS?

Well, if you are yet to do that – Let me help you get started.

Click through for a step-by-step walkthrough on how to create a Flink-centric HDInsight cluster on Azure Kubernetes Service and how to create a new job, assuming you have the Jarfile for that job already.

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Killing a Running Apache Spark Application

The Big Data in Real World team pulls the plug on an application:

Apache Spark is a powerful open-source distributed computing system used for big data processing. However, sometimes you may need to kill a running Spark application for various reasons, such as if the application is stuck, consuming too many resources, or taking too long to complete. In this post, we will discuss how to kill a running Spark application.

Click through to see how you can do this.

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Apache Kafka 3.6 Released

Satish Duggana announces what’s new in Apache Kafka 3.6:

The ability to migrate Kafka clusters from a ZooKeeper metadata system to a KRaft metadata system is now ready for usage in production environments. See the ZooKeeper to KRaft migration operations documentation for details. Note that support for JBOD is still not available for KRaft clusters, therefore clusters utilizing JBOD can not be migrated. See KIP-858 for details regarding KRaft and JBOD.

Support for Delegation Tokens in KRaft (KAFKA-15219) was completed in 3.6, further reducing the gap of features between ZooKeeper-based Kafka clusters and KRaft. Migration of delegation tokens from ZooKeeper to KRaft is also included in 3.6.

Tiered Storage is an early access feature. It is currently only suitable for testing in non-production environments. See the Early Access Release Notes for more details.

Read on for more details around what’s new in Apache Kafka.

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Apache Spark Execution Plan Analysis

Karthik Penikalapati digs into Spark SQL explain plans:

In this blog post, we will explore how the Explain Plan can be your secret weapon for debugging and optimizing Spark applications. We’ll dive into the basics and provide clear examples in Spark Scala to help you understand how to leverage this valuable tool.

All I’m saying is, if some company wants to create SQL Sentry Plan Explorer for Apache Spark, I’d be down with it. That loss of an intuitive and powerful graphical interface for execution plans is definitely a point of friction when working with Apache Spark and Spark SQL.

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