December, 2019

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10 Best and Free Machine Learning Courses, Online

KDnuggets

Getting ready to leap into the world of Data Science? Consider these top machine learning courses curated by experts to help you learn and thrive in this exciting field.

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Exploring ksqlDB with Twitter Data

Confluent

When KSQL was released, my first blog post about it showed how to use KSQL with Twitter data. Two years later, its successor ksqlDB was born, which we announced this […].

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Uber Infrastructure in 2019: Improving Reliability, Driving Customer Satisfaction

Uber Engineering

Every day around the world, millions of trips take place across the Uber network, giving users more reliable transportation through ridesharing, bikes, and scooters, drivers and truckers additional opportunities to earn, employees and employers more convenient business travel, and hungry … The post Uber Infrastructure in 2019: Improving Reliability, Driving Customer Satisfaction appeared first on Uber Engineering Blog.

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Building The DataDog Platform For Processing Timeseries Data At Massive Scale

Data Engineering Podcast

Summary DataDog is one of the most successful companies in the space of metrics and monitoring for servers and cloud infrastructure. In order to support their customers, they need to capture, process, and analyze massive amounts of timeseries data with a high degree of uptime and reliability. Vadim Semenov works on their data engineering team and joins the podcast in this episode to discuss the challenges that he works through, the systems that DataDog has built to power their business, and how

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Apache Airflow® Best Practices for ETL and ELT Pipelines

Whether you’re creating complex dashboards or fine-tuning large language models, your data must be extracted, transformed, and loaded. ETL and ELT pipelines form the foundation of any data product, and Airflow is the open-source data orchestrator specifically designed for moving and transforming data in ETL and ELT pipelines. This eBook covers: An overview of ETL vs.

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Open-Sourcing Metaflow, a Human-Centric Framework for Data Science

Netflix Tech

by David Berg , Ravi Kiran Chirravuri , Romain Cledat , Savin Goyal , Ferras Hamad , Ville Tuulos tl;dr Metaflow is now open-source! Get started at metaflow.org. Netflix applies data science to hundreds of use cases across the company, including optimizing content delivery and video encoding. Data scientists at Netflix relish our culture that empowers them to work autonomously and use their judgment to solve problems independently.

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Teradata Experts on the Top Tech Predictions for 2020

Teradata

Teradata's team of experts are chiming in on their top technology and business predictions for 2020 - from AI to Customer Experience to the Cloud. Read more!

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More Trending

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Apache Kafka Producer Improvements with the Sticky Partitioner

Confluent

The amount of time it takes for a message to move through a system plays a big role in the performance of distributed systems like Apache Kafka®. In Kafka, the […].

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Uber’s Data Platform in 2019: Transforming Information to Intelligence

Uber Engineering

Uber’s busy 2019 included our billionth delivery of an Uber Eats order, 24 million miles covered by bike and scooter riders on our platform, and trips to top destinations such as the Empire State Building, the Eiffel Tower, and the … The post Uber’s Data Platform in 2019: Transforming Information to Intelligence appeared first on Uber Engineering Blog.

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Building The Materialize Engine For Interactive Streaming Analytics In SQL

Data Engineering Podcast

Summary Transactional databases used in applications are optimized for fast reads and writes with relatively simple queries on a small number of records. Data warehouses are optimized for batched writes and complex analytical queries. Between those use cases there are varying levels of support for fast reads on quickly changing data. To address that need more completely the team at Materialize has created an engine that allows for building queryable views of your data as it is continually update

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DBLog: A Generic Change-Data-Capture Framework

Netflix Tech

Andreas Andreakis , Ioannis Papapanagiotou Overview Change-Data-Capture (CDC) allows capturing committed changes from a database in real-time and propagating those changes to downstream consumers [1][2]. CDC is becoming increasingly popular for use cases that require keeping multiple heterogeneous datastores in sync (like MySQL and ElasticSearch) and addresses challenges that exist with traditional techniques like dual-writes and distributed transactions [3][4].

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Apache Airflow®: The Ultimate Guide to DAG Writing

Speaker: Tamara Fingerlin, Developer Advocate

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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How Natural Language Processing Improves the Customer Experience

Teradata

Machine Learning algorithms help computers understand human languages. Learn how this application in Vantage enables businesses to understand their customers.

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Market Basket Analysis: A Tutorial

KDnuggets

This article is about Market Basket Analysis & the Apriori algorithm that works behind it.

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The Easiest Way to Install Apache Kafka and Confluent Platform – Using Ansible

Confluent

With Confluent Platform 5.3, we are actively embracing the rising DevOps movement by introducing CP-Ansible, our very own open source Ansible playbooks for deployment of Apache Kafka® and the Confluent […].

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Productionizing Distributed XGBoost to Train Deep Tree Models with Large Data Sets at Uber

Uber Engineering

Michelangelo , Uber’s machine learning (ML) platform, powers machine learning model training across various use cases at Uber, such as forecasting rider demand , fraud detection , food discovery and recommendation for Uber Eats , and improving the accuracy of … The post Productionizing Distributed XGBoost to Train Deep Tree Models with Large Data Sets at Uber appeared first on Uber Engineering Blog.

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Optimizing The Modern Developer Experience with Coder

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.

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Solving Data Lineage Tracking And Data Discovery At WeWork

Data Engineering Podcast

Summary Building clean datasets with reliable and reproducible ingestion pipelines is completely useless if it’s not possible to find them and understand their provenance. The solution to discoverability and tracking of data lineage is to incorporate a metadata repository into your data platform. The metadata repository serves as a data catalog and a means of reporting on the health and status of your datasets when it is properly integrated into the rest of your tools.

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Netflix Hack Day?—?November 2019

Netflix Tech

Netflix Hack Day?—?Fall 2019 By Tom Richards , Carenina Garcia Motion , and Leslie Posada Hack Day at Netflix is an opportunity to build and show off a feature, tool, or quirky app. The goal is simple: experiment with new ideas/technologies, engage with colleagues across different disciplines, and have fun! We know even the silliest idea can spur something more.

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Data Analytics: How to Know the Right Business Questions to Ask

Teradata

Identifying and focusing on priority analytic use cases within your organization will ensure you are asking the right business questions. Find out more.

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Data Science Curriculum Roadmap

KDnuggets

What follows is a set of broad recommendations, and it will inevitably require a lot of adjustments in each implementation. Given that caveat, here are our curriculum recommendations.

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15 Modern Use Cases for Enterprise Business Intelligence

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?

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What’s New in Apache Kafka 2.4

Confluent

On behalf of the Apache Kafka® community, it is my pleasure to announce the release of Apache Kafka 2.4.0. This release includes a number of key new features and improvements […].

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How Dataquest Made the Difference for Stacey’s Data Job

Dataquest

Today, Stacey Ustian is a data engineer. But the path that led her here wasn’t always easy, and there were a few bumps and twists along the way. Her journey to data science started in a rather unusual place: the law library. After earning her Master’s degree in Library and Information Science, Stacey had taken a job working in the library of a law firm.

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SnowflakeDB: The Data Warehouse Built For The Cloud

Data Engineering Podcast

Summary Data warehouses have gone through many transformations, from standard relational databases on powerful hardware, to column oriented storage engines, to the current generation of cloud-native analytical engines. SnowflakeDB has been leading the charge to take advantage of cloud services that simplify the separation of compute and storage. In this episode Kent Graziano, chief technical evangelist for SnowflakeDB, explains how it is differentiated from other managed platforms and traditiona

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DBLog: A Generic Change-Data-Capture Framework

Netflix Tech

Andreas Andreakis , Ioannis Papapanagiotou Overview Change-Data-Capture (CDC) allows capturing committed changes from a database in real-time and propagating those changes to downstream consumers [1][2]. CDC is becoming increasingly popular for use cases that require keeping multiple heterogeneous datastores in sync (like MySQL and ElasticSearch) and addresses challenges that exist with traditional techniques like dual-writes and distributed transactions [3][4].

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Prepare Now: 2025s Must-Know Trends For Product And Data Leaders

Speaker: Jay Allardyce, Deepak Vittal, Terrence Sheflin, and Mahyar Ghasemali

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.

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Don’t Organize for AI, Organize for Analytics

Teradata

How do you organize your business for analytics? Here are six steps your enterprise should take when creating an analytics team. Read more!

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10 Free Top Notch Machine Learning Courses

KDnuggets

Are you interested in studying machine learning over the holidays? This collection of 10 free top notch courses will allow you to do just that, with something for every approach to improving your machine learning skills.

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Celebrating 1,000 Employees and Looking Towards the Path Ahead

Confluent

During the holiday season, it’s a particularly relevant time to pause, reflect, and celebrate, both the days past and those ahead. Here at Confluent, it’s a noticeably nostalgic moment, given […].

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Superset Announces Elasticsearch Support!

Preset

Announcing Elasticsearch in Superset, powered by a new open-source Python library from Preset

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How to Drive Cost Savings, Efficiency Gains, and Sustainability Wins with MES

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.

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Organizing And Empowering Data Engineers At Citadel

Data Engineering Podcast

Summary The financial industry has long been driven by data, requiring a mature and robust capacity for discovering and integrating valuable sources of information. Citadel is no exception, and in this episode Michael Watson and Robert Krzyzanowski share their experiences managing and leading the data engineering teams that power the business. They shared helpful insights into some of the challenges associated with working in a regulated industry, organizing teams to deliver value rapidly and re

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Data Compression for Large-Scale Streaming Experimentation

Netflix Tech

Julie (Novak) Beckley, Andy Rhines, Jeffrey Wong, Matthew Wardrop, Toby Mao, Martin Tingley Ever wonder why Netflix works so well when you’re streaming at home, on the train, or in a foreign hotel? Behind the scenes, Netflix engineers are constantly striving to improve the quality of your streaming service. The goal is to bring you joy by delivering the content you love quickly and reliably every time you watch.

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Don’t Organize for AI, Organize for Analytics

Teradata

How do you organize your business for analytics? Here are six steps your enterprise should take when creating an analytics team. Read more!

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What is the most important question for Data Science (and Digital Transformation)

KDnuggets

With so many buzzwords surrounding AI and machine learning, understanding which can bring business value and which are best left in the lab to mature is difficult. While machine learning offers significant power in driving digital transformations, a business must start with the right questions and leave the math to the development teams.

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Improving the Accuracy of Generative AI Systems: A Structured Approach

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.