KDnuggets

JANUARY 16, 2020

With integrations of multiple emerging technologies just in the past year, AI development continues at a fast pace. Following the blueprint of science and technology advancements in 2019, we predict 10 trends we expect to see in 2020 and beyond.

Technology 160

Technology 160

Uber Engineering

JANUARY 15, 2020

Uber leverages real-time analytics on aggregate data to improve the user experience across our products, from fighting fraudulent behavior on Uber Eats to forecasting demand on our platform. . As Uber’s operations became more complex and we offered additional features and … The post Engineering SQL Support on Apache Pinot at Uber appeared first on Uber Engineering Blog.

SQL 134

SQL Engineering Aggregated Data Data 134

Data Engineering Podcast

JANUARY 26, 2020

Summary Data pipelines are complicated and business critical pieces of technical infrastructure. Unfortunately they are also complex and difficult to test, leading to a significant amount of technical debt which contributes to slower iteration cycles. In this episode James Campbell describes how he helped create the Great Expectations framework to help you gain control and confidence in your data delivery workflows, the challenges of validating and monitoring the quality and accuracy of your dat

Data Pipeline 100

Data Pipeline PostgreSQL Media Data Validation 100

Confluent

JANUARY 23, 2020

A preview of Confluent Tiered Storage is now available in Confluent Platform 5.4, enabling operators to add an additional storage tier for data in Confluent Platform. If you’re curious about […].

Data 122

Data 122

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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.

Teradata

JANUARY 27, 2020

People want control over their personal data, but are also willing to trade it away for convenience. When does the exploitation of our data become unethical? Read more!

IT 115

IT Data 115

Grouparoo

JANUARY 11, 2020

In the last post , I made a case that the way to make the biggest difference in a metric like retention is to increase how many tests you can run each month. It turns out, going from 1 to 4 tests a month makes a huge difference, especially as those cohorts build on each other over time. To prove this out, I built a spreadsheet. Because I learned even more from creating the spreadsheet itself than writing the blog post, I thought I'd give those learnings some airtime, too.

Building 52

Building IT Process Data 52

Rockset

JANUARY 28, 2020

Walk into a store, grab the items you want, and walk out without having to interact with a cashier or even use a self-checkout system. That’s the no-hassle shopping experience of the future you’ll get at the Standard Store , a demonstration store showcasing the AI-powered checkout pioneered by Standard Cognition. The company makes use of computer vision to remove the need for checkout lines of any sort in physical retail locations.

Retail 40

Retail Google Cloud Raw Data Data Lake 40

Data Engineering Podcast

JANUARY 20, 2020

Summary Building a reliable data platform is a neverending task. Even if you have a process that works for you and your business there can be unexpected events that require a change in your platform architecture. In this episode the head of data for Mayvenn shares their experience migrating an existing set of streaming workflows onto the Ascend platform after their previous vendor was acquired and changed their offering.

Kafka 100

Kafka Media Architecture Data Pipeline 100

Confluent

JANUARY 22, 2020

I am pleased to announce the release of Confluent Platform 5.4. Like any new release of Confluent Platform, it’s packed with features. To make them easier to digest, I want […].

118

118

Teradata

JANUARY 20, 2020

The hybrid cloud is not just a consideration, but for many of our customers, already a reality. Read more to learn best practices when considering a hybrid or multi-cloud environment.

Cloud 87

Cloud 87

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.

Grouparoo

JANUARY 9, 2020

At Grouparoo , we have been interviewing a lot of marketers. The overall learning is that it's a hard job. The biggest reason is that they need data to make their campaigns work and do not have the means to get that data. Basically, they need Engineers to prioritize writing code to get the data into the tool they are using. That rarely happens.

Coding 52

Coding Project Engineering Building 52

KDnuggets

JANUARY 7, 2020

Follow this overview of Natural Language Generation covering its applications in theory and practice. The evolution of NLG architecture is also described from simple gap-filling to dynamic document creation along with a summary of the most popular NLG models.

Architecture 159

Architecture IT Process 159

Rockset

JANUARY 23, 2020

A Brief History of Distributed Databases The era of Web 2.0 brought with it a renewed interest in database design. While traditional RDBMS databases served well the data storage and data processing needs of the enterprise world from their commercial inception in the late 1970s until the dotcom era, the large amounts of data processed by the new applications—and the speed at which this data needs to be processed—required a new approach.

Database 40

Database MySQL Kafka NoSQL 40

Data Engineering Podcast

JANUARY 13, 2020

Summary The modern era of software development is identified by ubiquitous access to elastic infrastructure for computation and easy automation of deployment. This has led to a class of applications that can quickly scale to serve users worldwide. This requires a new class of data storage which can accomodate that demand without having to rearchitect your system at each level of growth.

SQL 100

SQL PostgreSQL MongoDB Database Design 100

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.

Data Science

Confluent

JANUARY 14, 2020

Part 1 of this series discussed the basic elements of an event streaming platform: events, streams, and tables. We also introduced the stream-table duality and learned why it is a […].

Kafka 94

Kafka IT Process Data 94

Teradata

JANUARY 12, 2020

Learn about the different ways to use R and Python with Vantage and the pros and cons of each option. Read more from our Teradata expert.

Python 80

Python SQL 80

KDnuggets

JANUARY 9, 2020

This book is thought for beginners in Machine Learning, that are looking for a practical approach to learning by building projects and studying the different Machine Learning algorithms within a specific context.

Machine Learning 158

Machine Learning Algorithm Project Building 158

KDnuggets

JANUARY 7, 2020

An estimated 8,650% growth of the volume of Data to 175 zetabytes from 2010 to 2025 has created an enormous need for Data Engineers to build an organization's big data platform to be fast, efficient and scalable.

Data Engineering 158

Data Engineering Data Engineer Engineering Big Data 158

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.

Building

KDnuggets

JANUARY 20, 2020

It’s easy to say "I wanna be a data scientist," but. where do you start? How much time is needed to be desired by companies? Do you need a Master’s degree? Do you need to know every mathematical concept ever derived? The journey might be long, but follow this plan to help you keep moving forward toward your career goal.

Data 158

Data 158

KDnuggets

JANUARY 2, 2020

Time series forecasting is a technique for the prediction of events through a sequence of time. In this post, we will be taking a small forecasting problem and try to solve it till the end learning time series forecasting alongside.

IT 149

IT Python 149

KDnuggets

JANUARY 17, 2020

This article will tell you about the top 9 mobile apps that help the user in learning and practicing data science and hence is improving their productivity.

Data Science 155

Data Science Data 155

KDnuggets

JANUARY 10, 2020

A resume plays a key role in bagging that dream data science job. We break down the nuances of a job-winning data science resume so that you can go ahead and transform your own resume.

Data Science 143

Data Science Data 143

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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.

Building

KDnuggets

JANUARY 3, 2020

Why do most data scientists love Python? Learn more about how so many well-developed Python packages can help you accomplish your crucial data science tasks.

Data Science 148

Data Science Python Data Programming Language 148

KDnuggets

JANUARY 22, 2020

Preparing for a job interview can be a full-time job, and Data Science interviews are no different. Here are 121 resources that can help you study and quiz your way to landing your dream data science job.

Data Science 135

Data Science Data 135

KDnuggets

JANUARY 17, 2020

This summary overviews the keynote at TensorFlow World by Jeff Dean, Head of AI at Google, that considered the advancements of computer vision and language models and predicted the direction machine learning model building should follow for the future.

Machine Learning 132

Machine Learning Building 132

Data Engineering Podcast

JANUARY 5, 2020

Summary Databases are useful for inspecting the current state of your application, but inspecting the history of that data can get messy without a way to track changes as they happen. Debezium is an open source platform for reliable change data capture that you can use to build supplemental systems for everything from maintaining audit trails to real-time updates of your data warehouse.

Database 100

Database Kafka PostgreSQL MySQL 100

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.

Engineering

KDnuggets

JANUARY 16, 2020

This post is about fast-tracking the study and explanation of tree concepts for the data scientists so that you breeze through the next time you get asked these in an interview.

Algorithm 116

Algorithm Data Science Data 116

KDnuggets

JANUARY 6, 2020

Reducing images to numbers makes them amenable to computation. Let's take a look at the why and the how using Python and Numpy.

Python 139

Python Process 139

KDnuggets

JANUARY 9, 2020

Data Scientists work with tons of data, and many times that data includes natural language text. This guide reviews 7 common techniques with code examples to introduce you the essentials of NLP, so you can begin performing analysis and building models from textual data.

Data 159

Data Coding Building Data Preparation 159

KDnuggets

JANUARY 20, 2020

Pursuing a text analytics path but don't know where to start? Try this string processing primer to first gain an understanding of using Python to manipulate and process strings at a basic level.

Python 121

Python Process Data Process Programming 121

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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