May, 2022

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Data Engineering Project for Beginners - Batch edition

Start Data Engineering

1. Introduction 2. Objective 3. Design 4. Setup 4.1 Prerequisite 4.2 AWS Infrastructure costs 4.3 Data lake structure 5. Code walkthrough 5.1 Loading user purchase data into the data warehouse 5.2 Loading classified movie review data into the data warehouse 5.3 Generating user behavior metric 5.4. Checking results 6. Tear down infra 7. Design considerations 8.

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Azure Data Factory: How to edit default parameter definition for ARM templates?

Azure Data Engineering

ARM or Azure Resource Manager templates make it easy to manage deployments for Data Factory. When we connect Data Factory to a source control repository (e.g. GitHub or Azure DevOps Git), the data factory along with all its artefacts ( pipelines , datasets , linked services etc.) is saved in the repository in the form of ARM templates. We can then create DevOps pipelines to manage deployments by overriding the parameters to deploy to the production environments.

Datasets 130
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Cloud Native Data Orchestration For Machine Learning And Data Engineering With Flyte

Data Engineering Podcast

Summary Machine learning has become a meaningful target for data applications, bringing with it an increase in the complexity of orchestrating the entire data flow. Flyte is a project that was started at Lyft to address their internal needs for machine learning and integrated closely with Kubernetes as the execution manager. In this episode Ketan Umare and Haytham Abuelfutuh share the story of the Flyte project and how their work at Union is focused on supporting and scaling the code and communi

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The Complete Collection of Data Science Books – Part 2

KDnuggets

Read the best books on Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, MLOps, Robotics, IoT, AI Products Management, and Data Science for Executives.

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Get Better Network Graphs & Save Analysts Time

Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

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What’s New in Apache Kafka 3.2.0

Confluent

I’m proud to announce the release of Apache Kafka 3.2.0 on behalf of the Apache Kafka® community. The 3.2.0 release contains many new features and improvements. This blog will highlight […].

Kafka 139
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AI-First Benefits: 5 Real-World Outcomes

Cloudera

Artificial intelligence (AI) has been a focus for research for decades, but has only recently become truly viable. The availability and maturity of automated data collection and analysis systems is making it possible for businesses to implement AI across their entire operations to boost efficiency and agility. AI has the potential to transform operations by improving three fundamental business requirements: process automation, decision-making based on data insights, and customer interaction.

Insurance 129

More Trending

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Azure Data Factory: Stored Procedure Activity

Azure Data Engineering

When it comes to transforming structured data, (e.g., applying business logic, standardization etc.) stored in a database, SQL is the most convenient and fit-to-purpose option. Stored procedures provide a way to store the transformation logic as a set of SQL statements that can be re-executed as pre-compiled code. The Stored Procedure Activity in Data Factory provides and simple and convenient way to execute Stored Procedures.

SQL 130
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Data Cloud Cost Optimization With Bluesky Data

Data Engineering Podcast

Summary The latest generation of data warehouse platforms have brought unprecedented operational simplicity and effectively infinite scale. Along with those benefits, they have also introduced a new consumption model that can lead to incredibly expensive bills at the end of the month. In order to ensure that you can explore and analyze your data without spending money on inefficient queries Mingsheng Hong and Zheng Shao created Bluesky Data.

Cloud 100
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Top Posts May 23-29: The Complete Collection of Data Science Books – Part 2

KDnuggets

Also: Decision Tree Algorithm, Explained; Data Science Projects That Will Land You The Job in 2022; The 6 Python Machine Learning Tools Every Data Scientist Should Know About; Naïve Bayes Algorithm: Everything You Need to Know.

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Confluent at a Fully Disconnected Edge

Confluent

Internet connectivity is something we sometimes take for granted. For many, most places we visit, work, or reside have some form of connectivity whether it be cellular, Wi-Fi, fiber, etc. […].

IT 130
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Understanding User Needs and Satisfying Them

Speaker: Scott Sehlhorst

We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.

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Audio Analysis With Machine Learning: Building AI-Fueled Sound Detection App

AltexSoft

We live in the world of sounds: Pleasant and annoying, low and high, quiet and loud, they impact our mood and our decisions. Our brains are constantly processing sounds to give us important information about our environment. But acoustic signals can tell us even more if analyze them using modern technologies. Today, we have AI and machine learning to extract insights, inaudible to human beings, from speech, voices, snoring, music, industrial and traffic noise, and other types of acoustic signals

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Optimizing Hive on Tez Performance

Cloudera

Tuning Hive on Tez queries can never be done in a one-size-fits-all approach. The performance on queries depends on the size of the data, file types, query design, and query patterns. During performance testing, evaluate and validate configuration parameters and any SQL modifications. It is advisable to make one change at a time during performance testing of the workload, and would be best to assess the impact of tuning changes in your development and QA environments before using them in product

Bytes 114
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A Survey of Causal Inference Applications at Netflix

Netflix Tech

At Netflix, we want to entertain the world through creating engaging content and helping members discover the titles they will love. Key to that is understanding causal effects that connect changes we make in the product to indicators of member joy. To measure causal effects we rely heavily on AB testing , but we also leverage quasi-experimentation in cases where AB testing is limited.

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Insights And Advice On Building A Data Lake Platform From Someone Who Learned The Hard Way

Data Engineering Podcast

Summary Designing a data platform is a complex and iterative undertaking which requires accounting for many conflicting needs. Designing a platform that relies on a data lake as its central architectural tenet adds additional layers of difficulty. Srivatsan Sridharan has had the opportunity to design, build, and run data lake platforms for both Yelp and Robinhood, with many valuable lessons learned from each experience.

Data Lake 100
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Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

Speaker: Timothy Chan, PhD., Head of Data Science

Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.

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How to Become a Machine Learning Engineer

KDnuggets

A machine learning engineer is a programmer proficient in building and designing software to automate predictive models. They have a deeper focus on computer science, compared to data scientists.

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How Walmart Uses Apache Kafka for Real-Time Replenishment at Scale

Confluent

Walmart’s global presence, with its vast number of retail stores plus its robust and rapidly growing e-commerce business, make it one of the most challenging retail companies on the planet […].

Retail 127
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Length of Stay in Hospital: How to Predict the Duration of Inpatient Treatment

AltexSoft

How many days will a particular person spend in a hospital? Healthcare facilities and insurance companies would give a lot to know the answer for each new admission. Today, we can employ AI technologies to predict the date of discharge. This article describes how data and machine learning help control the length of stay — for the benefit of patients and medical organizations.

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Winning With Data in the Fight Against Fraud, Waste, and Abuse

Cloudera

Fraud, waste, and abuse (FWA) in government is a constant, multi-billion dollar issue that challenges agency leaders at all levels and across all sectors, from healthcare to education to taxation to Social Security. The scope and scale of public spending — federal outlays alone were approximately $6.6 trillion in fiscal year 2020 according to the Congressional Budget Office — make FWA an inherently difficult problem to solve.

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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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New Strategies Needed to Manage Acute Part Shortages

Teradata

Faced with persistent supply chain disruption automotive companies need a new approach to planning. Find out more.

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Scaling Analysis of Connected Data And Modeling Complex Relationships With The TigerGraph Graph Database

Data Engineering Podcast

Summary Many of the events, ideas, and objects that we try to represent through data have a high degree of connectivity in the real world. These connections are best represented and analyzed as graphs to provide efficient and accurate analysis of their relationships. TigerGraph is a leading database that offers a highly scalable and performant native graph engine for powering graph analytics and machine learning.

Database 100
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The Definitive Guide To Switching Your Career Into Data Science

KDnuggets

Colossal amounts of data need to be dealt with by specialists. It’s no wonder then that the job prospects in this industry are expected to rise much faster than in other occupations.

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Making Confluent Cloud 10x More Elastic Than Apache Kafka

Confluent

Kafka is horizontally scalable, but it's not enough. So we made Confluent Cloud 10x more elastic - 10x faster to scale up to GB/s or down to zero, easier to use, and cost-effective.

Kafka 113
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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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How Monte Carlo and Snowflake Gave Vimeo a “Get Out Of Jail Free” Card For Data Fire Drills

Monte Carlo

This article is sourced based on the interview between Lior Solomon, (now the former) VP of Engineering, Data, at Vimeo with the co-founders of Firebolt on their Data Engineering Show podcast which took place August 18, 2021. Watch the full episode. Vimeo is a leading video hosting, sharing, and services platform provider. The 1,000+ company helps small, medium and enterprise businesses scale with the impact of video.

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Becoming AI-First: How to Get There

Cloudera

Deciding to adopt an AI-first strategy is the easy part. Figuring out how to implement it takes a little more effort. It requires a clear-eyed vision built around well-defined goals and a realistic execution plan. Being AI-first means setting up your organization for the future. By leveraging data, analytics, and automation, a company can gain a better understanding of where it is and where it needs to go.

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How can Airlines Meet the Needs of Today’s Digital Customer?

Teradata

The next generation of customers expects newer technologies & advanced self-service capabilities as the airline business becomes more competitive. How can airlines meet these expectations?

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Evolving And Scaling The Data Platform at Yotpo

Data Engineering Podcast

Summary Building a data platform is an iterative and evolutionary process that requires collaboration with internal stakeholders to ensure that their needs are being met. Yotpo has been on a journey to evolve and scale their data platform to continue serving the needs of their organization as it increases the scale and sophistication of data usage. In this episode Doron Porat and Liran Yogev explain how they arrived at their current architecture, the capabilities that they are optimizing for, an

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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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The Complete Collection of Data Science Books – Part 1

KDnuggets

Read the best books on Programming, Statistics, Data Engineering, Web Scraping, Data Analytics, Business Intelligence, Data Applications, Data Management, Big Data, and Cloud Architecture.

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Confluent Cloud: Making an Apache Kafka Service 10x Better

Confluent

What we’ve done to evolve from cloud Kafka to Confluent Cloud, a data streaming platform that’s 10X better than Kafka in elasticity, storage, resiliency, and more.

Kafka 95
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Available Only Till Stocks Last. Employable Only Till Skills Are Relevant

U-Next

Time is the only changing constant and with time, everything changes. Emotions, people and markets. Every now and then in our lives, there comes a time of disruption. Where routines are rattled and we are introduced to new things. . While this often sounds exciting, what these sudden changes put an end to are existing conventions and practices. . We are living at one such time. .

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#ClouderaLife Spotlight: Margot Tien, Software Engineer

Cloudera

From fashion to data flow, in this #ClouderaLife Spotlight Margot talks about her career transition from fashion design to cloud computing and her co-founding of Cloudera’s Asian American and Pacific Islander community Employee Resource Group amid the racial tensions of 2021. . It started with feeling stuck and ended with a brand-new career (BTW, lots of hard work in the middle).

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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.