Can’t-miss sessions for AWS Summit Chicago

Stackery

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. AWS Summit Chicago on the horizon, and while there’s no explicit serverless track, there are some amazing sessions to check out.

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.

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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

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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Schema Evolution Patterns

Speaker: Alex Rasmussen, CEO, Bits on Disk

If you want to make your development team squirm, ask them about database schema changes or API versioning. Most development teams struggle with changing database schemas and updating API versions without breaking existing code. Alex Rasmussen is an expert in helping teams through these struggles. His talk will examine database schema changes and API versioning as two instances of schema evolution: how your systems respond when the structure of your structured data changes.

Relevant Data

DevOps.com

The post Relevant Data appeared first on DevOps.com. Blogs ROELBOB

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Komprise: Elastic Data Migration

The New Stack

Data management software vendor Komprise has enhanced its ability to enable customers to migrate data across heterogeneous storage and cloud environments. The reason we call it Elastic Data Migration is it does a few different things. Data Kubernetes Storage Profile

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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.

Unravel Data Earns Certification for Cloudera Data Platform

DevOps.com

The post Unravel Data Earns Certification for Cloudera Data Platform appeared first on DevOps.com. Latest News Releases Unravel Data

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Redefining Data Protection

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

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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.

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.

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New Data Architectures are too Data-Store-Centric

Data Virtualization

Too often the design of new data architectures is based on old principles: they are still very data-store-centric. They consist of many physical data stores in which data is stored repeatedly and redundantly.

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.

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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.

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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.

Defining Data intelligence: Intelligence about Data, Not from Data

IDC

IDC has been using the phrase “data intelligence software” to describe a category of capabilities that provide intelligence about data, and the term “data intelligence” has caught on in the industry. But not all definitions of data intelligence are equal.

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Announcing Dell EMC Innovations in Data Protection and Data Management

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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Survey Finds More Data Disruption Occurring

DevOps.com

petabytes (PB) of data in 2019, a nearly 40% increase since 2018. The same survey also finds that as the amount of data has increased, so, too, has the cost of downtime. The post Survey Finds More Data Disruption Occurring appeared first on DevOps.com.

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

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.

Easy Data Transform v1.1.0

Successful Software

of Easy Data Transform this week. Now if you want to multiply two columns of your data together in Easy Data Transform, you can just do this: You can also access Javascript maths, date and string functions. Haven’t tried Easy Data Transform yet? I released v1.1.0

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Six Steps Every Business Should Take to Protect Its Data

DevOps.com

Data may or may not be the new oil but it is the growth engine of today’s digital age. Data about a company’s customers, products, services and sales is worth a lot to the company itself, of course, but also to its competitors, as well as to business-savvy hackers.

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Data: the New Currency That Accelerates Business

DevOps.com

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 DevOps.com.

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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.

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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?)

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.

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Data Management: Design Principals

Dell EMC

In the recent blog “Redefining Data Protection,” Sharad Rastogi discusses unlocking the value of data capital by evolving data protection to that of a leverageable service to help drive business outcomes. In this context, data protection transitions to data management.

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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

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Data Mapping and GDPR: How Are They Related?

DevOps.com

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.

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What Users Want: How and Why to Build Knowledge into Your Product

Speaker: Nils Davis, Principal, NPD Associates

Usage data allows PMs, the product team, and the whole organization to make better decisions. Good usage intelligence gives you the ability to be smarter, more active, more decisive, nimbler, and to minimize risk. But what if you don't have that data - such as before you have users? Or, what if the right decision seems to fly in the face of the data you have? Or, what if your product offers more than just the standard features? To get deeper into these questions, Nils Davis asks, "What is the most interesting thing about Instagram?" (Because who doesn't like a product that Facebook paid $1 billion for when it had fewer than 50 employees and no revenue?) Nils will use the example of Instagram’s Filters to talk about how putting prebuilt knowledge in your product can change the way your product is used for the better - putting you in the company of most market-leading products. Finally, he’ll tie it all together by explaining how the way you interpret and use usage data can impact the way your tell your product’s story, and ultimately, how your users use your product.

Primer: Demystifying Data Science

The New Stack

Recently he’s been increasingly involved in data science and AI projects. This is the first part of a series by Levon Paradzhanyan that demystifies data science, machine learning, deep learning, and artificial intelligence down while explaining how they all tie into one another.

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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

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Defining Data intelligence: Intelligence about Data, Not from Data

IDC

IDC has been using the phrase “data intelligence software” to describe a category of capabilities that provide intelligence about data, and the term “data intelligence” has caught on in the industry. But not all definitions of data intelligence are equal.

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DataOps and Beyond: How DevOps Methodology Transformed Our Approach to Data Science

DevOps.com

The post DataOps and Beyond: How DevOps Methodology Transformed Our Approach to Data Science appeared first on DevOps.com. Blogs DevOps Practice automation big data data management data science DataOps devops machine learning

Embedded BI and Analytics: Best Practices to Monetize Your Data

Speaker: Azmat Tanauli, Senior Director of Product Strategy at Birst

By creating innovative analytics products and expanding into new markets, more and more companies are discovering new potential revenue streams. Join Azmat Tanauli, Senior Director of Product Strategy at Birst, as he walks you through how data that you're likely already collecting can be transformed into revenue!

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

Martin Fowler

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