Start Data Engineering

JULY 11, 2020

Intro A very common use case in data engineering is to build a ETL system for a data warehouse, to have data loaded in from multiple separate databases to enable data analysts/scientists to be able to run queries on this data, since the source databases are used by your applications and we do not want these analytic queries to affect our application performance and the source data is disconnected as shown below.

Systems 130

Systems Designing ETL System Data Warehouse 130

Confluent

JULY 15, 2020

With billions of Internet of Things (IoT) devices, achieving real-time interoperability has become a major challenge. Together, Confluent, Waterstream, and MQTT are accelerating Industry 4.0 with new Industrial IoT (IIoT) […].

Kafka 139

Kafka Cloud Programming 139

Teradata

JULY 14, 2020

The days are gone when defining a user experience was limited to the choice of designers. Now data plays a more important role in the design process than ever before.

Designing 95

Designing Data Process 95

Netflix Tech

JULY 17, 2020

The cloud-based platform allows vendors, artists and creators to connect and collaborate on visual effects (VFX) from anywhere in the… Continue reading on Netflix TechBlog ».

Cloud 74

Cloud 74

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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.

Rockset

JULY 14, 2020

StoryFire is a social platform for content creators to share and monetize their stories and videos. Using Rockset to index data from their transactional MongoDB system , StoryFire powers complex aggregation and join queries for their social and leaderboard features. By moving read-intensive services off MongoDB to Rockset, StoryFire is able to solve two hard challenges: performance and scale.

MongoDB 52

MongoDB NoSQL SQL Architecture 52

Confluent

JULY 17, 2020

The first-ever virtual Kafka Summit 2020 kicks off next month in the comfort of your home office, couch, spare bedroom, living room, outbuilding, lanai, veranda, or in-home portico, featuring an […].

Kafka 137

Kafka 137

Zalando Engineering

JULY 16, 2020

The following guidelines and tricks help me with task management, time management, planning & prioritization, reacting to ad-hoc situations, and the sense of not having accomplished anything during the day. There is some overlap with our Remote Work Guidelines 1. My meta-advice for applying anything from this article: start with one improvement, don’t try it all at once.

Process 40

Process Management Systems Project 40

Preset

JULY 13, 2020

Funnels are a very popular mental model for understanding data. Learn the pros and cons of different funnel data visualizations using Apache Superset.

Data 40

Data 40

Confluent

JULY 16, 2020

Apache Kafka® is a distributed commit log, commonly used as a multi-tenant data hub to connect diverse source systems and sink systems. Source systems can be systems or records, operational […].

Kafka 70

Kafka Transportation Systems Data 70

Teradata

JULY 15, 2020

Teradata data scientists utilized Teradata technologies to develop models to accurately project the number of COVID-19 confirmations and deaths. Learn more.

Utilities 59

Utilities Technology Project Data 59

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.

Zalando Engineering

JULY 14, 2020

Challenges with our Tech Radar The Zalando Tech Radar is modelled after the Thoughtworks Technology Radar and includes a ring-based scoring for a certain technology/framework along with supplementary information about pros, cons, restrictions, usage, and lessons learned at Zalando available as a knowledge base for our teams. Since publishing, the approach and visualization engine has been used by others and also showcased at conferences as an example of how tech companies manage their technology

Technology 40

Technology Process Programming Language Programming 40

Data Engineering Podcast

JULY 13, 2020

Summary The first stage of every data pipeline is extracting the information from source systems. There are a number of platforms for managing data integration, but there is a notable lack of a robust and easy to use open source option. The Meltano project is aiming to provide a solution to that situation. In this episode, project lead Douwe Maan shares the history of how Meltano got started, the motivation for the recent shift in focus, and how it is implemented.

Data Integration 100

Data Integration Data Engineering Data Engineer Data 100

Rockset

JULY 16, 2020

MongoDB is the most popular NoSQL database today, by some measures, even taking on traditional SQL databases like MySQL, which have been the de facto standard for many years. MongoDB’s document model and flexible schemas allow for rapid iteration in applications. MongoDB is designed to scale out to massive datasets and workloads, so developers know they will not be limited by their database.

MongoDB 52

MongoDB IT NoSQL SQL 52

Teradata

JULY 16, 2020

Get step-by-step instructions to set up Teradata Vantage and author AWS Glue Streaming ETL jobs to stream data into Vantage from Amazon Kinesis and visualize the data.

AWS 52

AWS Data 52

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