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ksqlDB, the event streaming database, is becoming one of the most popular ways to work with Apache Kafka®. Every day, we answer many questions about the project, but here’s a […].
Cloudera delivers an enterprise data cloud that enables companies to build end-to-end data pipelines for hybrid cloud, spanning edge devices to public or private cloud, with integrated security and governance underpinning it to protect customers data. Cloudera has found that customers have spent many years investing in their big data assets and want to continue to build on that investment by moving towards a more modern architecture that helps leverage the multiple form factors.
Table of Contents Table of Contents Introduction Project description and requirements Infrastructure overview Apache Flink Apache Kafka Design Detect fraudulent accounts Log account actions Prerequisites Code Defining dependencies Inheritance Server logs generator Defining data flow in Apache Flink Create a streaming environment Creating a consumer to read events from Apache Kafka Detecting fraud and generating alert events Writing server logs to a PostgreSQL DB Fraud detection logic Open proces
"Where can I start if I want to become a Data Engineer?" This is a question I have heard many times before. My answer to it is actually always the same: Start doing a Data Engineering project! Choose a tool Your first step here should be to select a tool. Then start with that tool and then build the whole thing up. So you get some data and then start with a tool.
Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.
Summary Kafka has become a de facto standard interface for building decoupled systems and working with streaming data. Despite its widespread popularity, there are numerous accounts of the difficulty that operators face in keeping it reliable and performant, or trying to scale an installation. To make the benefits of the Kafka ecosystem more accessible and reduce the operational burden, Alexander Gallego and his team at Vectorized created the Red Panda engine.
Operating critical Apache Kafka® event streaming applications in production requires sound automation and engineering practices. Streaming applications are often at the center of your transaction processing and data systems, requiring […].
Operating critical Apache Kafka® event streaming applications in production requires sound automation and engineering practices. Streaming applications are often at the center of your transaction processing and data systems, requiring […].
Performance is one of the key, if not the most important deciding criterion, in choosing a Cloud Data Warehouse service. In today’s fast changing world, enterprises have to make data driven decisions quickly and for that they rely heavily on their data warehouse service. . In this blog post, we compare Cloudera Data Warehouse (CDW) on Cloudera Data Platform (CDP) using Apache Hive-LLAP to Microsoft HDInsight (also powered by Apache Hive-LLAP) on Azure using the TPC-DS 2.9 benchmark.
Analytics at Netflix: Who We Are and What We Do An Introduction to Analytics and Visualization Engineering at Netflix by Molly Jackman & Meghana Reddy Explained: Season 1 (Photo Credit: Netflix) Across nearly every industry, there is recognition that data analytics is key to driving informed business decision-making. But there is far less agreement on what that term “data analytics” actually means?
In which regions or countries is there a boom in the field of Data Sciences and thus a large number of jobs? This is a very interesting question, which newcomers or graduates often ask themselves. Maybe you have already asked yourself this question? The USA as an advanced country Companies in the USA are obviously very, very advanced with Data Science.
Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage
There’s a lot of noise surrounding the ability of AI agents to connect to your tools, systems and data. But building an AI application into a reliable, secure workflow agent isn’t as simple as plugging in an API. As an engineering leader, it can be challenging to make sense of this evolving landscape, but agent tooling provides such high value that it’s critical we figure out how to move forward.
Summary Data engineering is a constantly growing and evolving discipline. There are always new tools, systems, and design patterns to learn, which leads to a great deal of confusion for newcomers. Daniel Molnar has dedicated his time to helping data professionals get back to basics through presentations at conferences and meetups, and with his most recent endeavor of building the Pipeline Data Engineering Academy.
Near-real-time insights have become a de facto requirement for Azure use cases involving scalable log analytics, time series analytics, and IoT/telemetry analytics. Azure Data Explorer (also called Kusto) is the […].
Everybody needs more data and more analytics, with so many different and sometimes often conflicting needs. Data engineers need batch resources, while data scientists need to quickly onboard ephemeral users. Data architects deal with constantly evolving workloads and business analysts must balance the urgency and importance of a concurrent user population that continues to grow.
How we migrated our Android endpoints out of a monolith into a new microservice by Rohan Dhruva , Ed Ballot As Android developers, we usually have the luxury of treating our backends as magic boxes running in the cloud, faithfully returning us JSON. At Netflix, we have adopted the Backend for Frontend (BFF) pattern : instead of having one general purpose “backend API”, we have one backend per client (Android/iOS/TV/web).
Speaker: Andrew Skoog, Founder of MachinistX & President of Hexis Representatives
Manufacturing is evolving, and the right technology can empower—not replace—your workforce. Smart automation and AI-driven software are revolutionizing decision-making, optimizing processes, and improving efficiency. But how do you implement these tools with confidence and ensure they complement human expertise rather than override it? Join industry expert Andrew Skoog as he explores how manufacturers can leverage automation to enhance operations, streamline workflows, and make smarter, data-dri
"Since I started exploring Data Engineering, it has been overwhelming. In the end I have the feeling of giving up." This is a message that reached me from a viewer on YouTube. And that's exactly how I feel sometimes! Sometimes I feel a bit overwhelmed by the whole thing. Because there is so much going on. All the technology and Data Science hype. There is always something new on the horizon.
Summary In memory computing provides significant performance benefits, but brings along challenges for managing failures and scaling up. Hazelcast is a platform for managing stateful in-memory storage and computation across a distributed cluster of commodity hardware. On top of this foundation, the Hazelcast team has also built a streaming platform for reliable high throughput data transmission.
If you are taking your first steps with Apache Kafka®, looking at a test environment for your client application, or building a Kafka demo, there are two “easy button” paths […].
With Airflow being the open-source standard for workflow orchestration, knowing how to write Airflow DAGs has become an essential skill for every data engineer. This eBook provides a comprehensive overview of DAG writing features with plenty of example code. You’ll learn how to: Understand the building blocks DAGs, combine them in complex pipelines, and schedule your DAG to run exactly when you want it to Write DAGs that adapt to your data at runtime and set up alerts and notifications Scale you
For enterprise organizations, managing and operationalizing increasingly complex data across the business has presented a significant challenge for staying competitive in analytic and data science driven markets. With growing disparate data across everything from edge devices to individual lines of business needing to be consolidated, curated, and delivered for downstream consumption, it’s no wonder that data engineering has become the most in-demand role across businesses — growing at an estima
Part of our series on who works in Analytics at Netflix?—?and what the role entails by Julie Beckley & Chris Pham This Q&A provides insights into the diverse set of skills, projects, and culture within Data Science and Engineering (DSE) at Netflix through the eyes of two team members: Chris Pham and Julie Beckley. Photo from a team curling offsite?
Taking the definition of physical gravity & extending it to data analytics, we explore the opportunities to combine data gravity with analytic processing, at scale with Vantage.
Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.
Summary Databases are limited in scope to the information that they directly contain. For analytical use cases you often want to combine data across multiple sources and storage locations. This frequently requires cumbersome and time-consuming data integration. To address this problem Martin Traverso and his colleagues at Facebook built the Presto distributed query engine.
The ksqlDB team is pleased to announce ksqlDB 0.12.0. This release continues to improve upon the usability of ksqlDB and aims to reduce administration time. Highlights include query upgrades, which […].
Digital transformation has been on the agenda for a long time, but the sudden need to respond to the unprecedented challenges of 2020, has meant the buzzword has become an executable reality for many enterprises. I recently came across a KPMG report that revealed that 80% of executives are increasing investments on emerging technologies now, to drive higher realized value in the future.
Kamer Toker-Yildiz , Colin McFarland , Julia Glick At Netflix, when we can’t run A/B experiments we run quasi experiments ! We run quasi experiments with various objectives such as non-member experiments focusing on acquisition, member experiments focusing on member engagement, or video streaming experiments focusing on content delivery. Consolidating on one methodology could be a challenge, as we may face different design or data constraints or optimization goals.
Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?
The game had changed for the retail sector long ago – but it has taken the COVID-19 crisis for people to notice. A new appreciation for the role of data in retail has emerged.
According to over 40,000 developers, MongoDB is the most popular NOSQL database in use right now. The tool’s meteoric rise is likely due to its JSON structure which makes it easy for Javascript developers to use. From a developer perspective, MongoDB is a great solution for supporting modern data applications. Nevertheless, developers sometimes need to pull specific workflows out of MongoDB and integrate them into a secondary system while continuing to track any changes to the underlying MongoDB
Since the MongoDB Atlas source and sink became available in Confluent Cloud, we’ve received many questions around how to set up these connectors in a secure environment. By default, MongoDB […].
In this new webinar, Tamara Fingerlin, Developer Advocate, will walk you through many Airflow best practices and advanced features that can help you make your pipelines more manageable, adaptive, and robust. She'll focus on how to write best-in-class Airflow DAGs using the latest Airflow features like dynamic task mapping and data-driven scheduling!
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