2018

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Functional Data Engineering — a modern paradigm for batch data processing

Maxime Beauchemin

Batch data processing  — historically known as ETL —  is extremely challenging. It’s time-consuming, brittle, and often unrewarding. Not only that, it’s hard to operate, evolve, and troubleshoot. In this post, we’ll explore how applying the functional programming paradigm to data engineering can bring a lot of clarity to the process. This post distills fragments of wisdom accumulated while working at Yahoo, Facebook, Airbnb and Lyft, with the perspective of well over a decade of data warehousing

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Open-Source Data Warehousing – Druid, Apache Airflow & Superset

Simon Späti

These days, everyone talks about open-source. However, this is still not common in the Data Warehouse (DWH) field. Why is this? In my recent blog, I researched OLAP technologies, for this post I chose some open-source technologies and used them together to build a full data architecture for a Data Warehouse system. I went with Apache Druid for data storage, Apache Superset for querying and Apache Airflow as a task orchestrator.

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Octopai: Metadata Management for Better Business Intelligence with Amnon Drori - Episode 28

Data Engineering Podcast

Summary The information about how data is acquired and processed is often as important as the data itself. For this reason metadata management systems are built to track the journey of your business data to aid in analysis, presentation, and compliance. These systems are frequently cumbersome and difficult to maintain, so Octopai was founded to alleviate that burden.

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Observability at Scale: Building Uber’s Alerting Ecosystem

Uber Engineering

Uber’s software architectures consists of thousands of microservices that empower teams to iterate quickly and support our company’s global growth. These microservices support a variety of solutions, such as mobile applications, internal and infrastructure services, and products along with complex … The post Observability at Scale: Building Uber’s Alerting Ecosystem appeared first on Uber Engineering Blog.

Building 111
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From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

Speaker: Anne Steiner and David Laribee

As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineers’ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.

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Netflix OSS and Spring Boot?—?Coming Full Circle

Netflix Tech

Netflix OSS and Spring Boot?—?Coming Full Circle Taylor Wicksell, Tom Cellucci, Howard Yuan, Asi Bross, Noel Yap, and David Liu In 2007, Netflix started on a long road towards fully operating in the cloud. Much of Netflix’s backend and mid-tier applications are built using Java, and as part of this effort Netflix engineering built several cloud infrastructure libraries and systems?

Java 110
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Do These Things if you Want to Succeed as an HR Professional

U-Next

Success in today’s businesses has taken several meanings. Apart from just pay hikes and promotions, success has gotten new dimensions that have been of very recent origins. Today, success has become synonymous with happiness at a workplace, challenging tasks, compensatory rewards, incentives, authoritative job profiles, influential role, and more. The current talent pools in organizations have become wiser and more mature than their previous generation counterparts.

More Trending

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Creating Multi-language NLP Pipelines with Apache Spark

Domino Data Lab: Data Engineering

In this guest post, Holden Karau , Apache Spark Committer , provides insights on how to create multi-language pipelines with Apache Spark and avoid rewriting spaCy into Java. She has already written a complementary blog post on using spaCy to process text data for Domino. Karau is a Developer Advocate at Google as well as a co-author on High Performance Spark and Learning Spark.

Java 52
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Live Dashboards on Streaming Data - A Tutorial Using Amazon Kinesis and Rockset

Rockset

We live in a world where diverse systems—social networks, monitoring, stock exchanges, websites, IoT devices—all continuously generate volumes of data in the form of events, captured in systems like Apache Kafka and Amazon Kinesis. One can perform a wide variety of analyses, like aggregations, filtering, or sampling, on these event streams, either at the record level or over sliding time windows.

AWS 52
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One Audio Sequencer to Rule Them All

Pandora Engineering

Photo credit: Carol Yepes Last month Pandora announced a public podcast beta in conjunction with the Podcast Genome Project. This rollout introduced many exciting features to our current mobile application offerings, including fully integrated and native podcast support. Ironically, one of the most interesting features and perhaps our biggest engineering win with this iteration is something that’s transparent to our end users: the inclusion of a new audio playback sequencer used exclusively for

Media 52
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Open Source: November Review - Maintainer training, new releases and more

Zalando Engineering

Project Highlights ExternalDNS version 0.5.9 is ready for testing. This project allows you to control DNS records dynamically via Kubernetes resources in a DNS provider-agnostic way. ExternalDNS also successfully made its way to the Kubernetes Incubator. Check out the list of changes in this new release. Zalando-Incubator welcomed two brand new open source projects 1) Darty - a data dependency manager for data science projects.

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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.

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Announcing my session at #SQLBits - Azure Databricks

Advancing Analytics: Data Engineering

Simon Whiteley and I will be back at #SQLBits 2019 talking about hashtag#DataEngineering and #DataScience in Databricks. We will look at #ApacheSpark #Python #Engineering & #MachineLearning in this full day training day. Register Now Have you looked at Azure DataBricks yet? No! Then you need to. Why you ask, there are many reasons. The number 1, knowing how to use Apache Spark will earn you more money.

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OLAP, what’s coming next?

Simon Späti

Are you on the lookout for a replacement for the Microsoft Analysis Cubes, are you looking for a big data OLAP system that scales ad libitum, do you want to have your analytics updated even real-time? In this blog, I want to show you possible solutions that are ready for the future and fits into existing data architecture. What is OLAP? OLAP is an acronym for Online Analytical Processing.

Big Data 130
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Simplifying Continuous Data Processing Using Stream Native Storage In Pravega with Tom Kaitchuck - Episode 63

Data Engineering Podcast

Summary As more companies and organizations are working to gain a real-time view of their business, they are increasingly turning to stream processing technologies to fullfill that need. However, the storage requirements for continuous, unbounded streams of data are markedly different than that of batch oriented workloads. To address this shortcoming the team at Dell EMC has created the open source Pravega project.

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Maximizing Process Performance with Maze, Uber’s Funnel Visualization Platform

Uber Engineering

At Uber, we spend a considerable amount of resources making the driver sign-up experience as easy as possible. At Uber’s scale, even a one percent increase in the rate of sign-ups to first trips (the driver conversion rate) carries a … The post Maximizing Process Performance with Maze, Uber’s Funnel Visualization Platform appeared first on Uber Engineering Blog.

Process 110
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How to Build an Experimentation Culture for Data-Driven Product Development

Speaker: Margaret-Ann Seger, Head of Product, Statsig

Experimentation is often seen as an aspirational practice, especially at smaller, fast-moving companies who are strapped for time and resources. So, how can you get your team making decisions in a more data-driven way while continuing to remain lean and maintaining ship velocity? In this webinar, Margaret-Ann Seger, Head of Product at Statsig, will teach you how to build an experimentation culture from the ground-up, graduating from just getting started with data-driven development to operating

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Netflix Information Security: Preventing Credential Compromise in AWS

Netflix Tech

by Will Bengtson Previously we wrote about a method for detecting credential compromise in your AWS environment. The methodology focused on a continuous learning model and first use principle. This solution still is reactive in nature?—?we only detect credential compromise after it has already happened. Even with detection capabilities, there is a risk that exposed credentials can provide access to sensitive data and/or the ability to cause damage in our environment.

AWS 95
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Making slow queries fast with composite indexes in MySQL

nodeSWAT

Making slow queries fast using composite indexes in MySQL This post expects some basic knowledge of SQL. Examples were made using MySQL 5.7.18 and run on my mid 2014 Macbook Pro. Query execution times are based on multiple executions so index caching can kick in. The use-case came from a real application and the solution is used in production. So you have inserted preliminary data to your database and run a simple COUNT(*) query against it with a simple WHERE clause and… the spinner is still run

MySQL 52
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Meet the newest Data Superheros: The Sixth Annual Data Impact Awards Finalists Are…

Cloudera

Drum roll… Starting from well over 100 nominations, we are excited to announce the finalists for this year’s Data Impact Awards ! Each year, nominees have raised the bar, and this year is no exception. The level of impact that organizations have shown and the variety of use cases are inspiring. From AI models that power retail customer decision engines to utility meter analysis that disables underperforming gas turbines, these finalists demonstrate how machine learning and analytics have become

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Data Science vs Engineering: Tension Points

Domino Data Lab: Data Engineering

This blog post provides highlights and a full written transcript from the panel, “ Data Science Versus Engineering: Does It Really Have To Be This Way? ” with Amy Heineike , Paco Nathan , and Pete Warden at Domino HQ. Topics discussed include the current state of collaboration around building and deploying models, tension points that potentially arise, as well as practical advice on how to address these tension points.

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Entity Resolution Checklist: What to Consider When Evaluating Options

Are you trying to decide which entity resolution capabilities you need? It can be confusing to determine which features are most important for your project. And sometimes key features are overlooked. Get the Entity Resolution Evaluation Checklist to make sure you’ve thought of everything to make your project a success! The list was created by Senzing’s team of leading entity resolution experts, based on their real-world experience.

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Recap of Hadoop News for July 2018

ProjectPro

News on Hadoop - July 2018 Hadoop data governance services surface in wake of GDPR.TechTarget.com, July 2, 2018. GDPR has turned out to be a strong motivator that would bring greater governance to big data. At the recent DataWorks Summit 2018 , though most of the attention was focussed on how Hadoop pioneer Hortonworks is all set to expand its service in the cloud, there was great interest and importance put on managing data privacy as well.

Hadoop 52
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Programming Best Practices For Data Science

Dataquest

The data science life cycle is generally comprised of the following components: data retrieval data cleaning data exploration and visualization statistical or predictive modeling While these components are helpful for understanding the different phases, they don’t help us think about our programming workflow. Often, the entire data science life cycle ends up as an arbitrary mess of notebook cells in either a Jupyter Notebook or a single messy script.

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#NoEstimates

Zalando Engineering

Why I advocate a practice of no estimates as a software engineer Before I get to the topic, I would like to clarify one thing: I don’t want to ban estimations generally from software development, as there are good and solid reasons for it. In a nutshell, business needs to be predictable. I want to show a software developer's view on how to reduce or even get rid of endless estimations meetings with doubtful outcomes.

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AI at the Forefront of Digital Transformation Process in 2018

InData Labs

Digital Transformation Definition Digital transformation has been a big topic for a few years now, and it has many definitions. From a business perspective, digital transformation is about leveraging digital technologies to improve processes, competencies, and business models. It is also about changing the culture of the company because it requires letting go of old.

Process 52
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The Big Payoff of Application Analytics

Outdated or absent analytics won’t cut it in today’s data-driven applications – not for your end users, your development team, or your business. That’s what drove the five companies in this e-book to change their approach to analytics. Download this e-book to learn about the unique problems each company faced and how they achieved huge returns beyond expectation by embedding analytics into applications.

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New on Cloud Academy: Machine Learning on Google Cloud and AWS, Big Data Analytics, Terraform, and more

Cloud Academy

A 2017 IDC White Paper “recommend[s] that organizations that want to get the most out of cloud should train a wide range of stakeholders on cloud fundamentals and provide deep training to key technical teams ” (emphasis ours). Regular readers of the Cloud Academy blog know we’ve been talking about this for a long time. Future-proofing your organization requires technical excellence, collective experience, business context, and shared understanding.

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Continuously Query Your Time-Series Data Using PipelineDB with Derek Nelson and Usman Masood - Episode 62

Data Engineering Podcast

Summary Processing high velocity time-series data in real-time is a complex challenge. The team at PipelineDB has built a continuous query engine that simplifies the task of computing aggregates across incoming streams of events. In this episode Derek Nelson and Usman Masood explain how it is architected, strategies for designing your data flows, how to scale it up and out, and edge cases to be aware of.

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Databook: Turning Big Data into Knowledge with Metadata at Uber

Uber Engineering

From driver and rider locations and destinations, to restaurant orders and payment transactions, every interaction on Uber’s transportation platform is driven by data. Data powers Uber’s global marketplace, enabling more reliable and seamless user experiences across our products for riders, … The post Databook: Turning Big Data into Knowledge with Metadata at Uber appeared first on Uber Engineering Blog.

Metadata 110
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Cloud Nine: All Your Analytics, Wherever You Want Them. Really!

Teradata

Brian Wood explains how Teradata Vantage in the cloud has your back when it comes to analytic simplicity, control, effectiveness, and results.

Cloud 60
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The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Communication

Speaker: David Bard, Principal at VP Product Coaching

In the fast-paced world of digital innovation, success is often accompanied by a multitude of challenges - like the pitfalls lurking at every turn, threatening to derail the most promising projects. But fret not, this webinar is your key to effective product development! Join us for an enlightening session to empower you to lead your team to greater heights.

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Concurrency, MySQL and Node.js: A journey of discovery

nodeSWAT

Our story begins like so many others with a code loving protagonist — someone we all can relate to. His days are largely filled with designing code, writing code and reading about code — keeping clients happy while learning and having fun. This has been going on for years now with both MySQL and Node.js among others and as such our protagonist considers himself quite proficient with both those technologies.

MySQL 52
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Data Engineering is Critical to Big Data Success

Cloudera

I mentioned in an earlier blog titled, “Staffing your big data team, ” that data engineers are critical to a successful data journey. That said, most companies that are early in their journey lack a dedicated engineering group. And the longer it takes to put a team in place, the likelier it is that your big data project will stall. The data engineering team is responsible for collecting and ingesting batch and stream-oriented data, inventorying the data, working through ingest bottlenecks, and d

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Collaboration Between Data Science and Data Engineering: True or False?

Domino Data Lab: Data Engineering

This blog post includes candid insights about addressing tension points that arise when people collaborate on developing and deploying models. Domino’s Head of Content sat down with Don Miner and Marshall Presser to discuss the state of collaboration between data science and data engineering. The blog post provides distilled insights, audio clips, excerpted quotes as well as the full audio and written transcript.

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Recap of Hadoop News for June 2018

ProjectPro

News on Hadoop - June 2018 RightShip uses big data to find reliable vessels.HoustonChronicle.com,June 15, 2018. RightShip is using IBM’s predictive big data analytics platform to calculate the likelihood of compliance or mechanical troubles that an individual merchant ship will experience within the next year.It also leverages big data to analyse carbon emissions and vessel efficiency.

Hadoop 52
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Driving Business Impact for PMs

Speaker: Jon Harmer, Product Manager for Google Cloud

Move from feature factory to customer outcomes and drive impact in your business! This session will provide you with a comprehensive set of tools to help you develop impactful products by shifting from output-based thinking to outcome-based thinking. You will deepen your understanding of your customers and their needs as well as identifying and de-risking the different kinds of hypotheses built into your roadmap.