What’s in today’s newsletter:
Microsoft enables private high-speed Azure-AWS cloud connections 🌐
Roblox scales Kafka to 18 trillion daily messages ⟐
Databricks LTAP merges OLTP and OLAP workloads seamlessly⚡
Also, check out the weekly Deep Dive - The Way I Passed the Databricks Data Engineer Associate Exam
MULTICLOUD
TL;DR: Microsoft’s Azure MultiCloud Interconnect for AWS enables secure, high-performance private connections between Azure and AWS via ExpressRoute and Direct Connect, simplifying multicloud operations and boosting hybrid workload efficiency.
Microsoft launched Azure MultiCloud Interconnect for AWS, enabling private, high-bandwidth connections between Azure and AWS clouds.
The service uses Azure ExpressRoute and AWS Direct Connect to avoid public internet, enhancing security and performance.
It supports workloads like data replication, disaster recovery, and hybrid applications across Azure and AWS environments.
The solution reduces multicloud complexity and improves operational efficiency for enterprises adopting hybrid cloud strategies.
Why this matters: Azure MultiCloud Interconnect addresses the critical need for secure, reliable, and high-performance connections between Azure and AWS, enabling enterprises to optimize hybrid cloud setups. This reduces complexity and bolsters Microsoft’s competitive edge by embracing the growing reality of multicloud adoption in modern IT environments.
SPONSORED BY Svix
Consuming Webhooks Is Broken. Here Is How to Fix the Hardest Parts.
Building robust data pipelines and event-driven architectures means dealing with webhooks. But while the industry has focused heavily on making webhooks easier to send, consuming them remains a massive headache for engineering teams.
The hardest problems at the ingestion layer haven't gone away: managing complex signature verification, setting up local development environments, replaying failed events, and untangling out-of-order or duplicate payloads.
In their latest engineering breakdown, Svix highlights exactly why consuming webhooks is so difficult and provides actionable solutions for fixing each of these core problems.
Instead of wasting sprint cycles building custom boilerplate to handle ingestion edge cases, Svix handles the difficult infrastructure around retries, idempotency, security, and observability so you can focus on your core data platform.
See how the team behind the Standard Webhooks specification is fixing the inbound webhook experience.
APACHE KAFKA
Source Roblox: The Impact of End-to-End Optimization
TL;DR: Roblox scaled its Kafka platform to handle 18 trillion daily messages using bare metal hardware, tiered storage, and automation, ensuring reliable, high-throughput real-time data streaming for its growing multiplayer platform.
Roblox scaled its Kafka platform to process over 18 trillion messages daily, supporting massive user activity.
The company used bare metal hardware, tiered storage, and optimized broker configurations for high throughput.
Advanced monitoring and automation ensured reliability, addressing challenges like partitioning and message backpressure efficiently.
Roblox’s approach offers key lessons for managing hyper-scale real-time data streams in gaming and metaverse contexts.
Why this matters: Roblox’s ability to process 18 trillion daily messages using optimized open-source Kafka demonstrates how tailored infrastructure and automation can handle hyper-scale real-time data. This ensures seamless multiplayer experiences and sets a valuable precedent for other companies in gaming and metaverse industries managing massive data streams.
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DATABRICKS LTAP
TL;DR: Databricks' LTAP unifies OLTP and OLAP workloads in one scalable, ACID-compliant system, reducing complexity and costs while enabling real-time analytics and hybrid applications on a fresh, unified dataset.
LTAP unifies OLTP and OLAP workloads, breaking a 40-year-old database separation rule with a single system.
Databricks’ LTAP leverages Delta Lake and cloud-native architecture for scalable, ACID-compliant transactional and analytical processing.
This integration reduces complexity, lowers costs, and improves data freshness by eliminating separate systems and pipelines.
LTAP enables hybrid applications like real-time fraud detection and personalized recommendations with low latency and deep analytics.
Why this matters: LTAP’s unification of OLTP and OLAP workloads eliminates costly, complex data silos, enabling real-time analytics on fresh transactional data. This breakthrough empowers enterprises to innovate with hybrid applications, speeding decision-making and operational agility while aligning with scalable, cloud-native architectures for modern data strategies.
EVERYTHING ELSE IN CLOUD DATABASES
Rubrik unveils Apache Iceberg data protection on AWS
LibreDB Studio: Open-Source Self-Hosted SQL IDE
Swiggy boosts data speed by 96% using Snowflake
Data Mesh Drives AI-Ready Business Transformation
Fabric Data merges with TMT Insights in strategic deal
RavenDB Unveils AI Agent Tool Quill for SQL Data
Prefect Spotlights Security in FastMCP Authentication
Prefect Unveils dbt Orchestrator for Faster Data Pipes
AWS MCP Server now supports serverless deployment
Strands launches durable DynamoDB storage for agents
Ataccama lets agents verify data quality fast
12-Year-Old PostgreSQL Bug Risks Server Takeover
Airflow, Dagster, Prefect: Choosing Top Workflow Tools
Critical ArangoDB flaws expose data risks
Yuki Launches Cortex for Snowflake Monitoring
DEEP DIVE
How I passed the Databricks Data Engineer Associate Exam
To be quite honest, this exam was fairly challenging, unlike a 3 hour AWS monolith, which I have done twice for the ML and Data Analytics realm, which are the two most nightmarishly difficult certifications exams I have done in 24 years (and I have done many).
I am just going from memory at this point as it was last week at this time that I was smiling quite broadly, and evoking my latent, post exam Ric Flair persona.
You would probably be on the floor laughing if you ever saw me 10 minutes after passing a certification, strutting like an 1980s wrestler in a suburban, industrial parking lot.
What you need for this exam is to be able to quite succinctly get rid of distractors. You will be able to hone that skill if you do the Udemy practice exam from Derar Alhussein:
The actual preparation course is good, as well as the book also:
As you can see, Derar is very busy training us folks…
As time goes on, and with access to a multitude of AI chatbots, I am able to go through exams in a fast clip. Here are a couple of stellar hints. The first is to take the practice exams, and with the first pass of each exam, plug in your results, both questions and your answers to discover your weak spots, into the chatbots. Even Databricks has a document that makes mention of this method, somewhat.
My next tip is to make notes. A lot of notes. This way, you have a written record of the study material, and I would focus again on the week spots. Nothing too earth shattering.
All in all, I focused on the practice exams. The structure of them were very close to the actual exam, and quite honestly, I found them slightly more challenging than the exam itself. There are 4 sets of the Udemy practice tests with 55 questions per test, and I did 3 passes of each until I was getting over 90% correct answers.
Again, focusing on your weak points until you have an excellent understanding of them, will help you pass the exam with flying colours.
Next week I will go over the exam domains, one by one, and let you know what is important on the exam.
Gladstone Benjamin
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