Start Data Engineering
SEPTEMBER 29, 2021
1. Introduction 2. Dbt, the T in ELT 3. Project 3.1. Prerequisites 3.2. Configurations and connections 3.2.1. profiles.yml 3.2.2. dbt_project.yml 3.3 Data flow 3.3.1. Source 3.3.2. Snapshots 3.3.3. Staging 3.3.4. Marts 3.3.4.1. Core 3.3.4.2. Marketing 3.4. dbt run 3.5. dbt test 3.6. dbt docs 3.7. Scheduling 4. Conclusion 5. Further reading 6. References 1.
Simon Späti
SEPTEMBER 28, 2021
Taking notes helps you not to forget things, teaches you to express yourself, brainstorms your thoughts, research a topic, and so many more things. I used to take notes all my life. Maybe it’s because I’m Swiss, they say we are well organised. I used to write in OneNote for 10+ years. I have notebooks for my bachelor studies and every workplace I worked.
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Marc Lamberti
SEPTEMBER 21, 2021
By default, your tasks get executed once all the parent tasks succeed. this behaviour is what you expect in general. But what if you want something more complex? What if you would like to execute a task as soon as one of its parents succeeds? Or maybe you would like to execute a different set of tasks if a task fails? Or act differently according to if a task succeeds, fails or event gets skipped?
Confluent
SEPTEMBER 21, 2021
I’m pleased to announce the release of Apache Kafka 3.0 on behalf of the Apache Kafka® community. Apache Kafka 3.0 is a major release in more ways than one. Apache […].
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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?
Uber Engineering
SEPTEMBER 2, 2021
Introduction. Uber’s GSS (Global Scaled Solutions) team runs scaled programs for diverse products and businesses, including but not limited to Eats, Rides, and Freight. The team transforms Uber’s ideas into agile, global solutions by designing and implementing scalable solutions. One … The post Streaming Real-Time Analytics with Redis, AWS Fargate, and Dash Framework appeared first on Uber Engineering Blog.
Data Engineering Podcast
SEPTEMBER 29, 2021
Summary The promise of online services is that they will make your life easier in exchange for collecting data about you. The reality is that they use more information than you realize for purposes that are not what you intended. There have been many attempts to harness all of the data that you generate for gaining useful insights about yourself, but they are generally difficult to set up and manage or require software development experience.
Data Engineering Digest brings together the best content for data engineering professionals from the widest variety of industry thought leaders.
Cloudera
SEPTEMBER 15, 2021
Leveraging the Internet of Things (IoT) allows you to improve processes and take your business in new directions. But it requires you to live on the edge. That’s where you find the ability to empower IoT devices to respond to events in real time by capturing and analyzing the relevant data. Edge computing relies on squeezing the power and functionality of a data center into a micro site as close to data sources as possible to enable real-time tasks.
Teradata
SEPTEMBER 12, 2021
Groupon is modernizing with Vantage on AWS to better match its data & analytics with demands of its global business. The Cloud allows Groupon to better leverage infrastructure dollars, support more technology projects and capture opportunity.
Confluent
SEPTEMBER 16, 2021
The full inventory of three online Kafka Summits in 2021 is now complete. Kafka Summit Americas wrapped just yesterday. Being a part of the event team and the Program Committee, […].
Uber Engineering
SEPTEMBER 29, 2021
Introduction. The Fulfillment Platform is a foundational Uber domain that enables the rapid scaling of new verticals. The platform handles billions of database transactions each day, ranging from user actions (e.g., a driver starting a trip) and system actions … The post Building Uber’s Fulfillment Platform for Planet-Scale using Google Cloud Spanner appeared first on Uber Engineering Blog.
Speaker: Jay Allardyce, Deepak Vittal, and Terrence Sheflin
As we look ahead to 2025, business intelligence and data analytics are set to play pivotal roles in shaping success. Organizations are already starting to face a host of transformative trends as the year comes to a close, including the integration of AI in data analytics, an increased emphasis on real-time data insights, and the growing importance of user experience in BI solutions.
Data Engineering Podcast
SEPTEMBER 25, 2021
Summary The accuracy and availability of data has become critically important to the day-to-day operation of businesses. Similar to the practice of site reliability engineering as a means of ensuring consistent uptime of web services, there has been a new trend of building data reliability engineering practices in companies that rely heavily on their data.
DataKitchen
SEPTEMBER 20, 2021
Data organizations don’t always have the budget or schedule required for DataOps when conceived as a top-to-bottom, enterprise-wide transformational change. An essential part of the DataOps methodology is Agile Development , which breaks development into incremental steps. DataOps can and should be implemented in small steps that complement and build upon existing workflows and data pipelines.
Cloudera
SEPTEMBER 20, 2021
There are many ways that Apache Kafka has been deployed in the field. In our Kafka Summit 2021 presentation, we took a brief overview of many different configurations that have been observed to date. In this blog series, we will discuss each of these deployments and the deployment choices made along with how they impact reliability. In Part 1, the discussion is related to: Serial and Parallel Systems Reliability as a concept, Kafka Clusters with and without Co-Located Apache Zookeeper, and Kafka
Netflix Tech
SEPTEMBER 22, 2021
Martin Tingley with Wenjing Zheng , Simon Ejdemyr , Stephanie Lane , and Colin McFarland This is the second post in a multi-part series on how Netflix uses A/B tests to inform decisions and continuously innovate on our products. See here for Part 1: Decision Making at Netflix. Subsequent posts will go into more details on the statistics of A/B tests, experimentation across Netflix, how Netflix has invested in infrastructure to support and scale experimentation, and the importance of the culture
Speaker: Nikhil Joshi, Founder & President of Snic Solutions
Is your manufacturing operation reaching its efficiency potential? A Manufacturing Execution System (MES) could be the game-changer, helping you reduce waste, cut costs, and lower your carbon footprint. Join Nikhil Joshi, Founder & President of Snic Solutions, in this value-packed webinar as he breaks down how MES can drive operational excellence and sustainability.
Confluent
SEPTEMBER 24, 2021
We’re pleased to announce ksqlDB 0.21.0! This release includes a major upgrade to ksqlDB’s foreign-key joins, the new data type BYTES, and a new ARRAY_CONCAT function. All of these features […].
Uber Engineering
SEPTEMBER 23, 2021
Uber recently launched a new capability: Ads on UberEats. With this new ability came new challenges that needed to be solved at Uber, such as systems for ad auctions, bidding, attribution, reporting, and more. This article focuses on how we … The post Real-Time Exactly-Once Ad Event Processing with Apache Flink, Kafka, and Pinot appeared first on Uber Engineering Blog.
Data Engineering Podcast
SEPTEMBER 24, 2021
Summary Python has beome the de facto language for working with data. That has brought with it a number of challenges having to do with the speed and scalability of working with large volumes of information.There have been many projects and strategies for overcoming these challenges, each with their own set of tradeoffs. In this episode Ehsan Totoni explains how he built the Bodo project to bring the speed and processing power of HPC techniques to the Python data ecosystem without requiring any
DataKitchen
SEPTEMBER 17, 2021
Below is our final post (5 of 5) on combining data mesh with DataOps to foster innovation while addressing the challenges of a data mesh decentralized architecture. We see a DataOps process hub like the DataKitchen Platform playing a central supporting role in successfully implementing a data mesh. DataOps excels at the type of workflow automation that can coordinate interdependent domains, manage order-of-operations issues and handle inter-domain communication.
Speaker: Anindo Banerjea, CTO at Civio & Tony Karrer, CTO at Aggregage
When developing a Gen AI application, one of the most significant challenges is improving accuracy. This can be especially difficult when working with a large data corpus, and as the complexity of the task increases. The number of use cases/corner cases that the system is expected to handle essentially explodes. 💥 Anindo Banerjea is here to showcase his significant experience building AI/ML SaaS applications as he walks us through the current problems his company, Civio, is solving.
Cloudera
SEPTEMBER 1, 2021
The more an enterprise wants to know about itself and its business prospects, the more data it needs to collect and analyze. Additionally, the more data it collects and stores, the better its ability to know customers, to find new ones, and to provide more of what they want to buy. Sounds simple, but a surprising majority of U.S. companies (about two-thirds, according to CIO.com ) are only now getting tuned in to become fully functioning data-driven enterprises by starting new initiatives, scali
Netflix Tech
SEPTEMBER 3, 2021
By Alex Borysov , Ricky Gardiner Background At Netflix, we heavily use gRPC for the purpose of backend to backend communication. When we process a request it is often beneficial to know which fields the caller is interested in and which ones they ignore. Some response fields can be expensive to compute, some fields can require remote calls to other services.
Confluent
SEPTEMBER 30, 2021
The rise of fully managed cloud services fundamentally changed the technology landscape and introduced benefits like increased flexibility, accelerated deployment, and reduced downtime. Confluent offers a portfolio of fully managed […].
Uber Engineering
SEPTEMBER 9, 2021
Problem. Uber deploys a few storage technologies to store business data based on their application model. One such technology is called Schemaless , which enables the modeling of related entries in one single row of multiple columns, as well as … The post Jellyfish: Cost-Effective Data Tiering for Uber’s Largest Storage System appeared first on Uber Engineering Blog.
Speaker: Donna Laquidara-Carr, PhD, LEED AP, Industry Insights Research Director at Dodge Construction Network
In today’s construction market, owners, construction managers, and contractors must navigate increasing challenges, from cost management to project delays. Fortunately, digital tools now offer valuable insights to help mitigate these risks. However, the sheer volume of tools and the complexity of leveraging their data effectively can be daunting. That’s where data-driven construction comes in.
Data Engineering Podcast
SEPTEMBER 19, 2021
Summary Biology has been gaining a lot of attention in recent years, even before the pandemic. As an outgrowth of that popularity, a new field has grown up that pairs statistics and compuational analysis with scientific research, namely bioinformatics. This brings with it a unique set of challenges for data collection, data management, and analytical capabilities.
Teradata
SEPTEMBER 19, 2021
Teradata Vantage on Azure supports 27 business services across supply chain, sales, finance, HR, and more.
Cloudera
SEPTEMBER 21, 2021
Many customers looking at modernizing their pipeline orchestration have turned to Apache Airflow, a flexible and scalable workflow manager for data engineers. With 100s of open source operators, Airflow makes it easy to deploy pipelines in the cloud and interact with a multitude of services on premise, in the cloud, and across cloud providers for a true hybrid architecture. .
Netflix Tech
SEPTEMBER 28, 2021
Written by Michael Possumato , Nick Tomlin , Jordan Andree , Andrew Shim , and Rahul Pilani. As we continue to grow here at Netflix, the needs of Revenue and Growth Engineering are rapidly evolving; and our tools must also evolve just as rapidly. The Revenue and Growth Tools (RGT) team decided to set off on a journey to build tools in an abstract manner to have solutions readily available within our organization.
Speaker: Aindra Misra, Senior Manager, Product Management (Data, ML, and Cloud Infrastructure) at BILL
Join us for an insightful webinar that explores the critical intersection of data privacy and AI governance. In today’s rapidly evolving tech landscape, building robust governance frameworks is essential to fostering innovation while staying compliant with regulations. Our expert speaker, Aindra Misra, will guide you through best practices for ensuring data protection while leveraging AI capabilities.
Confluent
SEPTEMBER 15, 2021
GraphQL and Apache Kafka® are sometimes troubled with misconceptions. One of the reasons for this is that people are often familiar with one but not the other. GraphQL is mostly […].
DataKitchen
SEPTEMBER 14, 2021
The post What Should Enterprises Do to Offset Future Technology Disruption? first appeared on DataKitchen.
Data Engineering Podcast
SEPTEMBER 19, 2021
Summary Building, scaling, and maintaining the operational components of a machine learning workflow are all hard problems. Add the work of creating the model itself, and it’s not surprising that a majority of companies that could greatly benefit from machine learning have yet to either put it into production or see the value. Tristan Zajonc recognized the complexity that acts as a barrier to adoption and created the Continual platform in response.
Datakin
SEPTEMBER 14, 2021
Blog Building a Remote-First Culture Written by Amanda Bulger on Sep 15, 2021 This morning I was planning an offsite for our team – our first one since Datakin was founded during the pandemic – and I had a realization: I haven’t met most of these people in person yet! We’ve been working together for months and months, solving interesting problems and planning social events, but we have been restricted to knowing each other through a tiny box on a screen.
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Entity Resolution Sometimes referred to as data matching or fuzzy matching, entity resolution, is critical for data quality, analytics, graph visualization and AI. Learn what entity resolution is, why it matters, how it works and its benefits. Advanced entity resolution using AI is crucial because it efficiently and easily solves many of today’s data quality and analytics problems.
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