Sat.Apr 10, 2021 - Fri.Apr 16, 2021

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Flipr: Making Changes Quickly and Safely at Scale

Uber Engineering

Introduction. Uber’s many software systems require a high volume of changes every day. Because of our systems’ size and complexity, it is a significant challenge to implement these changes without unintended consequences, ultimately slowing down developer productivity. Flipr is a … The post Flipr: Making Changes Quickly and Safely at Scale appeared first on Uber Engineering Blog.

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Building the Confluent UI with React Hooks – Benefits and Lessons Learned

Confluent

Updating a fundamental paradigm in your React app can be as easy as search and replace, or at other times, as difficult as convincing your entire frontend engineering to buy […].

Building 125
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Exploring The Expanding Landscape Of Data Professions with Josh Benamram of Databand

Data Engineering Podcast

Summary "Business as usual" is changing, with more companies investing in data as a first class concern. As a result, the data team is growing and introducing more specialized roles. In this episode Josh Benamram, CEO and co-founder of Databand, describes the motivations for these emerging roles, how these positions affect the team dynamics, and the types of visibility that they need into the data platform to do their jobs effectively.

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10 Upcoming Data Science Platforms for Massive Disruption

DataKitchen

The post 10 Upcoming Data Science Platforms for Massive Disruption first appeared on DataKitchen.

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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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Enabling NVIDIA GPUs to accelerate model development in Cloudera Machine Learning

Cloudera

When working on complex, or rigorous enterprise machine learning projects, Data Scientists and Machine Learning Engineers experience various degrees of processing lag training models at scale. While model training on small data can typically take minutes, doing the same on large volumes of data can take hours or even weeks. To overcome this, practitioners often turn to NVIDIA GPUs to accelerate machine learning and deep learning workloads. .

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Debuting a Modern C++ API for Apache Kafka

Confluent

Morgan Stanley uses Apache Kafka® to publish market data to internal clients and to persist it for replay purposes. We started out using librdkafka’s C++ API, which maintains C++98 compatibility. […].

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CFO Analytics - CFO of the Future

Teradata

As finance teams evolve into the providers of strategic insights, leveraging analytics will result in a new user base, new insights & reposition the CFO to a predictor of the future.

Finance 52
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Coffee with Cloudera – Gavin Welch, Sr. Partner Manager, IHVs

Cloudera

Meet Gavin Welch, Cloudera’s Partner Manager of the Year! Gavin leads the hardware alliances team and has made big strides in Cloudera’s relationship with Dell/EMC , launched a new strategic partnership with NVIDIA to help GPU acceleration for CDP-PvC Base, and recently became a first-time dad. Gavin’s the all-star quarterback everyone wants on their team, the guy you always see active on Slack in the middle of the night, and the same guy that’s chipper after a long call with legal, trying to ge

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Stream Processing is Nothing Without Action

Confluent

The transition from a passive event stream to an active component like a workflow engine is very interesting. It raises a lot of questions about idempotency, scalability, and the capability […].

Process 86
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Sync modes - Intentional data syncing

Grouparoo

Grouparoo supports syncing data to an ever-growing number of destinations. While building these integrations and talking to our users, we have found it's important to be intentional about how exactly data syncing to these destinations is performed. For example, our Salesforce data integration has a "Sync Mode" option that allows you to control whether contacts will be created, deleted or only updated.

Data 52
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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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Data Engineer, Data Analyst, Data Scientist — What’s the Difference?

Dataquest

Data engineer, data analyst, and data scientist — these are job titles you’ll often hear mentioned together when people are talking about the fast-growing field of data science. There are plenty of other job titles in data science and data analytics too. But here, we’re going to talk about: The “big three” roles (data analyst, data scientist, and data engineer) How they differ from each other Which role is best for you Although precisely how these roles are defined can va

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The Key to Unlocking IT Modernization’s Power? Enterprise level Transformation

Cloudera

The United States Veterans Administration (VA) over the last decade underwent a massive enterprise-wide IT transformation, eliminating its fragmented shadow IT and adopting a centralized system capable of supporting the agency’s 400,000 employees and more effectively utilizing its $240 billion-plus annual budget. The result: A more reliable and modern IT environment that improves access, availability, and user experience -ultimately supporting the VA mission more effectively.

IT 53
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Data Mesh and the Threads that Hold it Together

Teradata

Data mesh is gaining popularity as an approach to enterprise data architecture. While it embodies great ideas, there are a few principles that could easily be lost when applying it in real life.

IT 52
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How to Learn Data Science From Scratch on Your Own in 2023

ProjectPro

We decided to write this content piece because, in the past few months, many aspiring data science professionals asked our project advisors these questions on getting started with a data science career - “I want to learn data science but I don’t know where to start.” “I know python for web development but how to learn python for data science.” “What is the best way to learn data science?

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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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Don't Do Background Jobs on Google Cloud Run

Grouparoo

Grouparoo is a self-hosted product, so we are always looking for the simplest ways to help our customers run the application. A new member of the Google Cloud Platform (GCP) family is Google Cloud Run - which is the closest Google has come yet to a Heroku-like "Git-Ops" way to deploy your applications. It handles load balancing, scaling, and more for you and is a really compelling product.

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No Data Loss and No Service Interruption – HDF to CFM Rolling Migration

Cloudera

The blog “ Migrating Apache NiFi Flows from HDF to CFM with Zero Downtime ” detailed how many common NiFi dataflows can be easily migrated when the Hortonworks DataFlow and Cloudera Flow Management clusters are running side-by-side. But what if you lack the resources to run multiple NiFi clusters concurrently? Not a problem. Rolling migration — decommissioning your HDF NiFi nodes and recycling them for use in a CFM NiFi cluster — is an alternative solution to migrate these NiFi dataflows with t

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Connect Teradata Vantage to Salesforce Using Amazon Appflow

Teradata

Many Teradata customers are interested in integrating Vantage with AWS First Party Services. This guide will help you to connect Vantage to Salesforce using Amazon Appflow.

AWS 52
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Case Study: Bringing Real-Time Analytics to Construction Logistics at Command Alkon

Rockset

Construction projects are hives of constant activity, sustained by steady incoming streams of building materials. Think construction logistics and one pictures a flow of trucks transporting concrete and other necessary materials from suppliers to construction sites. Yet for every physical delivery made, many more exchanges of data occur in the background in order to seamlessly orchestrate supply chain operations.

NoSQL 40
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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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The DataOps Vendor Landscape, 2021

DataKitchen

Download the 2021 DataOps Vendor Landscape here. Read the complete blog below for a more detailed description of the vendors and their capabilities. DataOps is a hot topic in 2021. This is not surprising given that DataOps enables enterprise data teams to generate significant business value from their data. Companies that implement DataOps find that they are able to reduce cycle times from weeks (or months) to days, virtually eliminate data errors, increase collaboration, and dramatically imp

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10 Steps to Achieve Enterprise Machine Learning Success

Cloudera

You’ve probably heard it more than once: Machine learning (ML) can take your digital transformation to another level. It’s a pie-in-the-sky statement that sounds great, right? And while you’d be forgiven for thinking that it might sound too good to be true, operational ML is , in fact, achievable and sustainable. You can get the very kind of ML you need to increase revenue and lower costs.

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Achieving Insights and Savings with Cost Data

Airbnb Tech

The path to cloud efficiency begins with a cost data foundation by Anna Matlin and Tamar Eterman Introduction Business profitability and sustainability are powerful reasons to invest in infrastructure efficiency, but it is easy to feel lost about how to actually reduce costs. A foundation of robust and actionable data is essential for a successful efficiency program.

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Modeling Errors in GraphQL

Zalando Engineering

GraphQL Errors GraphQL is an excellent language for writing data requirements in a declarative fashion. It gives us a clear and well-defined concept of nullability constraints and error propagation. In this post, let's discuss how GraphQL lacks in certain places regarding errors and how we can model those errors to fit some of our use-cases. Before we dive into the topic, let's understand how GraphQL currently treats and handles errors.

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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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100 Deep Learning Interview Questions and Answers for 2023

ProjectPro

Deep learning job interviews. A necessary evil. Most beginners in the industry break out in a cold sweat at the mere thought of a machine learning or a deep learning job interview. How do I prepare for my upcoming deep learning job interview? What kind of deep learning interview questions they are going to ask me? What questions should I ask them? These are just a few thoughts that run through the mind of any interviewee.

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Cloudera Data Engineering – Integration steps to leverage spark on Kubernetes

Cloudera

What is Cloudera Data Engineering (CDE) ? Cloudera Data Engineering is a serverless service for Cloudera Data Platform (CDP) that allows you to submit jobs to auto-scaling virtual clusters. CDE enables you to spend more time on your applications, and less time on infrastructure. CDE allows you to create, manage, and schedule Apache Spark jobs without the overhead of creating and maintaining Spark clusters.

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Powering Real-Time Analytics at Scale on MySQL and PostgreSQL

Rockset

Relational databases today are widely known to be suboptimal for supporting high-scale analytical use cases, and are all but certain to run into issues as your production data size and query volume grow. This has been by far one of the most well-known weaknesses of relational databases for much of the past decade, and has led to surges in popularity of several new classes of databases such as NoSQL and NewSQL – each with their own sets of tradeoffs and drawbacks.

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5 Success Stories That Show the Value of Enterprise Data Cloud

Cloudera

What’s the fastest and easiest path towards powerful cloud-native analytics that are secure and cost-efficient? In our humble opinion, we believe that’s Cloudera Data Platform (CDP). And sure, we’re a little biased—but only because we’ve seen firsthand how CDP helps our customers realize the full benefits of public cloud. . Organizations from across the globe and virtually every industry have used CDP to generate new revenue streams, decrease operational costs, and mitigate risks.

Cloud 78
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Reimagined: Building Products with Generative AI

“Reimagined: Building Products with Generative AI” is an extensive guide for integrating generative AI into product strategy and careers featuring over 150 real-world examples, 30 case studies, and 20+ frameworks, and endorsed by over 20 leading AI and product executives, inventors, entrepreneurs, and researchers.

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What’s new in CDP Private Cloud Base 7.1.6?

Cloudera

According to IDG, when customers consider updating to the latest release of a product, they expect new features, enhanced security, and better performance, but increasingly want a more streamlined upgrade process. With each new release of CDP Private Cloud, this is exactly what we strive to deliver. Along with a host of new features and capabilities, we are improving the upgrade process to be as painless as possible.

Cloud 71