Can’t-miss sessions for AWS Summit Chicago


AWS Glue is a fully managed extract, transform, and load (ETL) service to prepare and load data for analytics. You can use Glue to generate ETL code in Scala or Python to extract data from the source, transform the data to match the target schema, and load it into the target.

Data engineers vs. data scientists

O'Reilly Media - Data

It’s important to understand the differences between a data engineer and a data scientist. Misunderstanding or not knowing these differences are making teams fail or underperform with big data. Overly simplistic venn diagram with data scientists and data engineers.

Self-serve data platform

Martin Fowler

One of the main concerns of distributing the ownership of data to the domains is the duplicated effort and skills required to operate the data pipelines technology stack and infrastructure in each domain.

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Domain-driven data architecture

Martin Fowler

Zhamak explains the first part of the data mesh concept - using the ideas behind Domain-Driven Design to structure the data platform. more…. skip-home-page

Make an Impact with Analytics and Journey Maps

Speaker: Kirui K. K., Co-founder and CEO of Tanasuk Africa

Book Review: Designing Data-Intensive Applications

Henrik Warne

What a great book Designing Data-Intensive Applications is! There are three parts in the book: Foundations of Data Systems (chapters 1 – 4), Distributed Data (chapters 5 – 9), and Derived Data (chapters 10 – 12). Foundations of Data Systems.

Data Versioning and Pipelines in CD4ML

Martin Fowler

My colleagues continue their article on Continuous Delivery for Machine Learning by looking at the future, considering what further work needs to be done in Data Versioning and Data Pipelines. more…. skip-home-page

Types of Data Structures

The Crazy Programmer

Data structures are a very important programming concept. They provide us with a means to store, organize and retrieve data in an efficient manner. The data structures are used to make working with our data, easier. There are many data structures which help us with this.

Data 275

Doing good data science

O'Reilly Media - Data

Data scientists, data engineers, AI and ML developers, and other data professionals need to live ethical values, not just talk about them. The hard thing about being an ethical data scientist isn’t understanding ethics. It’s doing good data science.

Data 208

Data's day of reckoning

O'Reilly Media - Data

Our lives are bathed in data: from recommendations about whom to “follow” or “friend” to data-driven autonomous vehicles. Although we’ve benefited from the use of data in countless ways, it has also created a tension between individual privacy, public good, and corporate profits.

Data 207

Monetizing Analytics Features: Why Data Visualizations Will Never Be Enough

MONETIZING ANALYTICS FEATURES: Why Data Visualizations. Data Visualizations Have Gone From Rare to Ubiquitous 1 If DataViz Is Old News, What’s the Future of Analytics? Ubiquitous Five years ago, data visualizations were a powerful way to diferentiate a software.

How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh

Martin Fowler

Many enterprises are investing in a centralized data platform to provide support for business insights and (hopefully) automated decision making. These demand a shift to a more decentralized approach that draws from modern distributed architecture - which she refers to as a data mesh.

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Data Mapping and GDPR: How Are They Related?

Every business enterprise receives data from an array of diverse data points. The volume of data is growing, and businesses understand the importance of leveraging data and converting them into actionable insights. This is where data mapping takes a precedence.

Data 94

The ethics of data flow

O'Reilly Media - Data

If we’re going to think about the ethics of data and how it’s used, then we have to take into account how data flows. Data, even “big data,” doesn’t stay in the same place: it wants to move. We give up our data all the time. Data flows can be very complex.

Data 186

Data: the New Currency That Accelerates Business

The amount of data created every day is staggering: 2.5 Cisco Systems estimates that by the end of 2019, IoT will generate more than 500 zettabytes of data per […]. The post Data: the New Currency That Accelerates Business appeared first on

Data 87

Your Post-Launch Toolkit for Understanding Your Users

Speaker: Brittney Gwynn, former Director of Product, Simple Health

5 Steps to Ensuring Validity of Your Business-Critical Data

Bad data can lead organizations to make mistakes. If your organization isn’t continuously ensuring data is accurate, you can never be sure the business decisions you’re making are smart. So, how do you continuously validate your business data without consuming excessive resources?

Data 70

Is It Time to Unify Your Data?

TIBCO - Connected Intelligence

Data Equals Opportunity. Digital businesses rely on data to drive compelling customer experiences, optimized business processes, and innovative new products. Or might you continue to get by with your current data management approaches? How can you benefit from unifying your data?

Data 67

The data imperative

O'Reilly Media - Data

Ben Sharma shares how the best organizations immunize themselves against the plague of static data and rigid process Continue reading The data imperative

Data 136

Redefining Data Protection

Armughan Ahmad - Dell EMC

In a world where digital transformation determines winners and losers, businesses continue to create increasingly larger volumes of data, and by way of doing so, have evolved to the point where every organization is now a technology company. Data Center Data Protection Opinions Dell EMC

Data 106

5 Early Indicators Your Embedded Analytics Will Fail

thrilled to finally visualize their data. They ask to explore data on their own, create and. share analysis, and connect new data sources to the. requests for new and more complex data visualizations, the ability to customize dashboards, and real-time.

The Value of Visual Data in Decision-Making

Instinct has ruled business leaders’ mind for a very long time, but as companies expand their business globally, instinct should give way to data as the best measure to remain competitive. We live in a data-driven world.

Data 103

Data architecture vs backend architecture

Erik Bernhardsson

A modern tech stack typically involves at least a frontend and backend but relatively quickly also grows to include a data platform. This typically grows out of the need for ad-hoc analysis and reporting but possibly evolves into a whole oil refinery of cronjobs, dashboards, bulk data copying, and much more. What generally pushes things into the data platform is (generally) that a number of things are. Why bother with a data platform? The data side: the wild west.

Simplifying Big Data Projects with Data Virtualization

Data Virtualization

According to Gartner, 60% of all the big data projects fail and according to Capgemini 70% of the big data projects are not profitable. There can only be one conclusion, big data projects are hard!

Cloud Complexities Hinder Effective Enterprise Data Management

But they’re learning that if APIs are the glue they use to build their systems, data is the fuel that drives these efforts forward. According to data management provider Actian, however, enterprises are experiencing significant challenges when it comes […].

Why “Build or Buy?” Is the Wrong Question for Analytics

commit to staffing significant resources in development, support, and keeping up with advances in data. Architecting (and Re-Architecting) So Everything Works Together: If the component you choose to bind data doesn’t work. anyone to analyze data, share insights, and make.

Differentiating via data science

O'Reilly Media - Data

Eric Colson explains why companies must now think very differently about the role and placement of data science in organizations. Continue reading Differentiating via data science

Data 146

Making data science useful

O'Reilly Media - Ideas

Cassie Kozyrkov explains how organizations can extract more value from their data. Continue reading Making data science useful

Data 86

3 Steps to Building Data Science Models

Have you ever considered supplementing your development skills with some data science? Pick Your Data Game Your first step in learning data science is to find a dataset that fits your […]. The post 3 Steps to Building Data Science Models appeared first on

Data 113

Product thinking in a data platform

Martin Fowler

Distribution of the data ownership and data pipeline implementation into the hands of the business domains raise an important concern around accessibility, usability and harmonization of distributed datasets. Zhamak explains that this is where the learning in applying product thinking and ownership of data assets come in handy.

Data 157

How to Package and Price Embedded Analytics

customers absolutely need advanced capabilities like embedded self-service and the means to pull new data sources into the. and the data feeding them—as well as trigger both. rely on—enabling anyone to analyze data when and where. HOW TO PACKAGE & PRICE EMBEDDED ANALYTICS.

How the Rise of Data and AI Have Redefined the Data-Driven Enterprise

Cloudera Engineering

The rise of data and AI promises many things: the power to interpret, to predict, to transform. However, until the enterprise learns how to manage and master the data being generated and apply that learning to actual business use cases, that promise remains a distant dream.

Why a data scientist is not a data engineer

O'Reilly Media - Ideas

A few months ago, I wrote about the differences between data engineers and data scientists. An interesting thing happened: the data scientists started pushing back, arguing that they are, in fact, as skilled as data engineers at data engineering. Data engineering !=

Announcing Dell EMC Innovations in Data Protection and Data Management

Armughan Ahmad - Dell EMC

Today I joined Jeff Clarke on stage at Dell Technologies World to announce major innovations in our data protection portfolio. Data Center Data Protection News Dell EMC

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What is Data Engineering: Explaining Data Pipeline, Data Warehouse, and Data Engineer Role


Being at the top of data science capabilities, machine learning and artificial intelligence are buzzing technologies many organizations are eager to adopt. Data science layers towards AI, Source: Monica Rogati. Explaining Data Engineering and Data Warehouse.

How King Crushes New Product Development using Data-Driven Insights

Speaker: Ian Thompson, Head of Business Intelligence at King, and Zara Wells, Strategic Customer Success Manager at Looker

Product Managers looking to leverage data to make informed product design decisions can learn a lot from renowned gaming company King, maker of Candy Crush and many other games - even if their product has seemingly no overlap with games. Don't miss King’s data expert (dare we say king?)

Data protection and innovation

O'Reilly Media - Data

Continue reading Data protection and innovation Eva Kaili outlines the fundamentals of GDPR and applications of blockchain.