Data Engineering Podcast

JANUARY 7, 2018

Summary PostGreSQL has become one of the most popular and widely used databases, and for good reason. The level of extensibility that it supports has allowed it to be used in virtually every environment. At Citus Data they have built an extension to support running it in a distributed fashion across large volumes of data with parallelized queries for improved performance.

PostgreSQL 100

PostgreSQL Big Data NoSQL Data 100

U-Next

JANUARY 9, 2018

Success in today’s businesses has taken several meanings. Apart from just pay hikes and promotions, success has gotten new dimensions that have been of very recent origins. Today, success has become synonymous with happiness at a workplace, challenging tasks, compensatory rewards, incentives, authoritative job profiles, influential role, and more. The current talent pools in organizations have become wiser and more mature than their previous generation counterparts.

Recruitment 73

Recruitment Technology Management Process 73

Cloudera

JANUARY 12, 2018

I mentioned in an earlier blog titled, “Staffing your big data team, ” that data engineers are critical to a successful data journey. That said, most companies that are early in their journey lack a dedicated engineering group. And the longer it takes to put a team in place, the likelier it is that your big data project will stall. The data engineering team is responsible for collecting and ingesting batch and stream-oriented data, inventorying the data, working through ingest bottlenecks, and d

Big Data 54

Big Data Data Engineering Data Engineer Engineering 54

Maxime Beauchemin

JANUARY 7, 2018

Batch data processing  — historically known as ETL —  is extremely challenging. It’s time-consuming, brittle, and often unrewarding. Not only that, it’s hard to operate, evolve, and troubleshoot. In this post, we’ll explore how applying the functional programming paradigm to data engineering can bring a lot of clarity to the process. This post distills fragments of wisdom accumulated while working at Yahoo, Facebook, Airbnb and Lyft, with the perspective of well over a decade of data warehousing

Data Engineering 183

Data Engineering Data Engineer Data Process Process 183

Advertisement

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.

Database

Dataquest

JANUARY 10, 2018

In previous blog posts , we have described the Postgres database and ways to interact with it using Python. Those posts provided the basics, but if you want to work with databases in production systems, then it is necessary to know how to make your queries faster and more efficient. To understand what efficiency means in Postgres, it’s important to learn how Postgres works under the hood.

Building 52

Building PostgreSQL Database Python 52

Zalando Engineering

JANUARY 8, 2018

Leveraging the full power of a functional programming language In Zalando Dublin, you will find that most engineering teams are writing their applications using Scala. We will try to explain why that is the case and the reasons we love Scala. This content is coming both from my own experience and the team I'm working with in building the new Zalando Customer Data Platform.

Scala 40

Scala Bytes Java Programming 40

Cloudera

JANUARY 9, 2018

This was an amazing year for our inaugural “Cybersecurity On Call” season. It was truly an honor hosting amazing guests as we explored the world of cybersecurity. From industry thought leaders, to New York Times best sellers, to hackers, I learned a ton about the future of cybersecurity and I hope you did as well. Today’s episode won’t be our usual programming, today is our end of the year special where we will dive into our top five tips from this year’s season.

Government 40

Government Programming IT Management 40

Zalando Engineering

JANUARY 10, 2018

We talk to Han and Kashif from Zalando Research Employer Branding Specialist Data Science, Nana Yamazaki catches up with the team using literal fashion icons in Deep Learning. Tell us about Fashion-MNIST. What did you want to accomplish? Fashion-MNIST is a freely available dataset of Zalando articles that most importantly has the same format as the MNIST dataset.

Deep Learning 52

Deep Learning Datasets Machine Learning Data Science 52

Cloudera

JANUARY 11, 2018

I often meet with our customers to help them understand how to connect modern technology to business success. The ever-present question at these encounters is “Where do I start?” For them, they may understand that they need a data-driven strategy or the culture may aim to take a shift to being guided by data. These are often goals set by the executive team with little guidance on how to execute or implement.

Data Warehouse 40

Data Warehouse Telecommunication Media Retail 40