Data Engineering Podcast

SEPTEMBER 3, 2018

Summary With the proliferation of data sources to give a more comprehensive view of the information critical to your business it is even more important to have a canonical view of the entities that you care about. Is customer number 342 in your ERP the same as Bob Smith on Twitter? Using master data management to build a data catalog helps you answer these questions reliably and simplify the process of building your business intelligence reports.

Data Management 100

Data Management Management Relational Database Business Intelligence 100

Cloudera

SEPTEMBER 7, 2018

The worst thing about an inside job is that once it’s detected, it’s usually too late. Early detection is critical to prevent considerable damage arising out of insider threats to the business. But that’s easier said than done! Whether it’s a rogue trader in a bank or brokerage or someone illegally sharing company intellectual property or intelligence, illegal insider actions put enterprises at risk of losing millions.

Machine Learning 52

Machine Learning Unstructured Data Portfolio Relational Database 52

Domino Data Lab: Data Engineering

SEPTEMBER 1, 2018

Introduction: New Monthly Series! Welcome to a new monthly series! I’ll summarize highlights from recent industry conferences, new open source projects, interesting research, great examples, amazing people, etc. – all pointed at how to level up your organization’s data science practices.

Data Science 52

Data Science Project Machine Learning Data 52

Zalando Engineering

SEPTEMBER 5, 2018

Adversarial texture distribution learning as a tool of artistic expression Deep learning is progressing fast these days. Despite advances that were expected to happen sooner or later (e.g. accurate face and speech recognition), there are some new developments that would have seemed like a pipe dream years ago: neural networks can now generate realistic images just by looking at few examples of their properties.

Deep Learning 40

Deep Learning Machine Learning Designing Process 40

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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.

Architecture

ProjectPro

SEPTEMBER 3, 2018

News on Hadoop - August 2018 Apache Hadoop: A Tech Skill That Can Still Prove Lucrative.Dice.com, August 2, 2018. In 2017, Gartner announced that organizations were spending close to $800 million on Hadoop distributions , even though only 14% of companies reported that they were relying on hadoop technology.However, several studies have revealed that the adoption and spending on hadoop technology continues to rise high through last year.Dice analysis demonstrates that jobs that intersect with Ha

Hadoop 40

Hadoop Retail Banking Telecommunication 40

Cloudera

SEPTEMBER 5, 2018

While the Internet of Things (IoT) represents a significant opportunity, IoT architectures are often rigid, complex to implement, costly, and create a multitude of challenges for organizations. First of all, in order to effectively pull together an end-to-end architecture for IoT, organizations must manage multiple vendor solutions, validate that they work together, integrate them to ensure the right functionality, and provide for future enhancement compatibility.

Architecture 50

Architecture Machine Learning Cloud Data Security 50

Cloudera

SEPTEMBER 6, 2018

This is the third installment in a 3 part series. The first installment provides a short background on anti-money laundering. The second installment examines common AML problems faced by financial institutions today. In this installment, we introduce an approach that carries AML well into the future. Part III: The future is now. Given what we know about current anti-money laundering systems, if we wanted to build one from scratch today, we might come up with the following requirements.

Machine Learning 41

Machine Learning Banking Big Data Data Science 41

Cloudera

SEPTEMBER 5, 2018

This is the second installment in a 3 part series. The first installment provides a short background on anti-money laundering. In this installment, we examine common AML problems faced by financial institutions today. The third installment introduces an approach that carries AML into the future. Part II: Current Challenges in AML. There are several key areas in the field of anti-money laundering (AML) that rely heavily on technology.

Machine Learning 40

Machine Learning Big Data Relational Database Finance 40

Zalando Engineering

SEPTEMBER 4, 2018

Zalando is known for its commitment to the open source world. Many of our engineers are active contributors of open source projects like PostgreSQL or Kubernetes. The Zalando tech department currently consists of more than 2,000 employees that manage over 200 delivery teams and virtual teams. Zalando engineers are from 77 nations and based out of various locations across Europe which makes us super international but also quite distributed.

IT 40

IT PostgreSQL Finance Software Engineer 40

Cloudera

SEPTEMBER 4, 2018

This is the first installment in a 3 part series. It provides a short background on anti-money laundering for the layperson. AML professionals may wish to skip this installment and go directly to the second and third parts. The second installment examines common AML problems faced by financial institutions today. The third installment introduces an approach that carries AML into the future.

Banking 44

Banking Finance Programming Government 44

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!

Data