Sat.Jan 09, 2021 - Fri.Jan 15, 2021

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Job conversion possibilities within Data Science

Team Data Science

Data science encompasses a range of fields, like data analysis, machine learning, statistics, computer science, infrastructure, and data architecture, and looking at how businesses are transforming on a day-to-day basis, we may infer that some data science jobs will be in high demand within the next ten years, there is a strong need for experts who understand the market demands, who can formulate a data-driven approach and then execute the way out.

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Property Based Testing Confluent Server Storage for Fun and Safety

Confluent

Confluent uses property-based testing to test various aspects of Confluent Server’s Tiered Storage feature. Tiered Storage shifts data from expensive local broker disks to cheaper, scalable object storage, thereby reducing […].

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Enabling Version Controlled Data Collaboration With TerminusDB

Data Engineering Podcast

Summary As data professionals we have a number of tools available for storing, processing, and analyzing data. We also have tools for collaborating on software and analysis, but collaborating on data is still an underserved capability. Gavin Mendel-Gleason encountered this problem first hand while working on the Sesshat databank, leading him to create TerminusDB and TerminusHub.

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Cloudera Data Warehouse Demonstrates Best-in-Class Cloud-Native Price-Performance

Cloudera

Introduction. Cloud data warehouses allow users to run analytic workloads with greater agility, better isolation and scale, and lower administrative overhead than ever before. With the ability to quickly provision on-demand and the lower fixed and administrative costs, the costs of operating a cloud data warehouse are driven mostly by the price-performance of the specific data warehouse platform.

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A Guide to Debugging Apache Airflow® DAGs

In Airflow, DAGs (your data pipelines) support nearly every use case. As these workflows grow in complexity and scale, efficiently identifying and resolving issues becomes a critical skill for every data engineer. This is a comprehensive guide with best practices and examples to debugging Airflow DAGs. You’ll learn how to: Create a standardized process for debugging to quickly diagnose errors in your DAGs Identify common issues with DAGs, tasks, and connections Distinguish between Airflow-relate

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Sort Lists in Scala with Tail Recursion

Rock the JVM

Master List Sorting with Tail Recursion in Scala: Enhance Your Functional Programming Skills and Boost Your Interview Readiness!

Scala 52
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Putting Apache Kafka to REST: Confluent REST Proxy 6.0

Confluent

Confluent Platform 6.0 was released last year bringing with it many exciting new features to Confluent REST Proxy. Before we dive into what was added, let’s first revisit what REST […].

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With Love, Cloudera 2020 Interns

Cloudera

Most companies strive to provide a quality internship experience. At Cloudera, we take it to another level offering work with cutting edge and open source technologies, product experimentation, meaningful mentorship and group activities. . Yes, we aim to give our early talent teams insight into the real world of work as it relates to their engineering careers, but we also want to give them an experience that can’t be beat.

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ADTs (Algebraic Data Types) in Scala

Rock the JVM

Discover ADTs (Algebraic Data Types) in Scala: Answers to all your questions about this essential concept

Scala 52
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The Missing Link in Cloud Costs

Teradata

We examine the main impact of cloud costs by comparing and contrasting when price is considered on data at rest versus data in movement. Read more.

Cloud 52
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Everything You Need to Know About Data Preparation

InData Labs

The fact that data is now called the “new oil” is true. There is considerable money being made by the ones who are cleverly utilizing it. Fundamentally, big data is unlike oil. With the help of machine learning, It provides a lot more than just profit – it offers understanding and insight, with one exception. Запись Everything You Need to Know About Data Preparation впервые появилась InData Labs.

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Mastering Apache Airflow® 3.0: What’s New (and What’s Next) for Data Orchestration

Speaker: Tamara Fingerlin, Developer Advocate

Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.

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Optimized joins & filtering with Bloom filter predicate in Kudu

Cloudera

Introduction. In database systems one of the most effective ways to improve performance is to avoid doing unnecessary work, such as network transfers and reading data from disk. One of the ways Apache Kudu achieves this is by supporting column predicates with scanners. Pushing down column predicate filters to Kudu allows for optimized execution by skipping reading column values for filtered out rows and reducing network IO between a client, like the distributed query engine Apache Impala, and Ku

Java 72
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Objects and Companions in Scala

Rock the JVM

This article is for beginner Scala programmers: an introduction to singleton objects and companion objects, exploring their uses, benefits, and best practices

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Moving to the Cloud, Do I Still Need a CASB Solution?

Teradata

As businesses move more of their workloads to the cloud, & with more sophisticated encryption & security measures, do they still need to have a Cloud Access Security Broker solution as well?

Cloud 52
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Simplify Data Access in Snowflake using Domino Data Lab

Domino Data Lab: Data Engineering

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Agent Tooling: Connecting AI to Your Tools, Systems & Data

Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage

There’s a lot of noise surrounding the ability of AI agents to connect to your tools, systems and data. But building an AI application into a reliable, secure workflow agent isn’t as simple as plugging in an API. As an engineering leader, it can be challenging to make sense of this evolving landscape, but agent tooling provides such high value that it’s critical we figure out how to move forward.

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Brick and Mortar Stores are Now Built Brick by Brick with Digital Insights

Cloudera

This blog is the final post of a 4-part series. You can read the first blog posts, here: 1. Get to Know Your Retail Customer: 2. Accelerating Customer Insight and Relevance ; Improving your Customer-Centric Merchandising with Location-based in-Store Merchandising ; and 3. Maximizing Supply Chain Agility through the “Last Mile” Commitment. Brick and Mortar Stores will Need to do it Differently to Stay Alive.

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Experimentation Platform at Zalando: Part 1 - Evolution

Zalando Engineering

Online controlled experimentation, aka A/B test, has been a golden standard for evaluating improvements in software systems. By changing one factor at a time, A/B test causally measures, from real users, whether one product variant is better than the other. As an increasingly important area in tech companies, experimentation platforms face -- apart from their scientific challenges -- many unique engineering problems.

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How to Make Regulatory Calls for Transparency a Competitive Advantage

Teradata

While on the surface transparency requirements appear solely as a nuisance, they are rich opportunities to repurpose investments in compliance for strategic advantage.

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100+ Machine Learning Datasets Curated For You

ProjectPro

Undoubtedly, everyone knows that the only best way to learn data science and machine learning is to learn them by doing diverse projects. And honestly, there are a lot of real-world machine learning datasets around you that you can opt to start practicing your fundamental data science and machine learning skills, even without having to complete a comprehensive data science or machine learning course.

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How to Modernize Manufacturing Without Losing Control

Speaker: Andrew Skoog, Founder of MachinistX & President of Hexis Representatives

Manufacturing is evolving, and the right technology can empower—not replace—your workforce. Smart automation and AI-driven software are revolutionizing decision-making, optimizing processes, and improving efficiency. But how do you implement these tools with confidence and ensure they complement human expertise rather than override it? Join industry expert Andrew Skoog as he explores how manufacturers can leverage automation to enhance operations, streamline workflows, and make smarter, data-dri

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Enabling Self-Service Business Insights with Cloudera Data Warehouse

Cloudera

Requests to Central IT for data warehousing services can take weeks or months to deliver. Central IT teams at large organizations face a proliferation of IT projects arising from the complexities of markets and from the needs of internal lines of business (LoBs). At the same time, Central IT must juggle cost and risk. In data-driven organizations, to fulfill its charter to democratize data and provide on-demand, quality computing services in a secure, compliant environment, IT must replace legac

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Top 5 Questions about Apache NiFi

Cloudera

Over the last few weeks, I delivered four live NiFi demo sessions, showing how to use NiFi connectors and processors to connect to various systems, with 1000 attendees in different geographic regions. I want to thank you all for joining and attending these events! Interactive demo sessions and live Q&A are what we all need these days when working remotely from home is now a norm.

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2020 Data Impact Award Winner Spotlight: United Overseas Bank

Cloudera

2020 was a year of immense change and disruption. Despite the challenges, 2020 also provided positive opportunities for forward leaps to be made in the realm of digital transformation. At Cloudera, an example of this leap is our first virtual Data Impact Awards , which was held in November last year. . One of our stand out moments of the awards was the introduction of the “Data Impact Achievement Award”.

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Building a Machine Learning Application With Cloudera Data Science Workbench And Operational Database, Part 2: Querying/ Loading Data

Cloudera

In this installment, we’ll discuss how to do Get/Scan Operations and utilize PySpark SQL. Afterward, we’ll talk about Bulk Operations and then some troubleshooting errors you may come across while trying this yourself. Read the first blog here. Get/Scan Operations. Using Catalogs. In this example, let’s load the table ‘tblEmployee’ that we made in the “Put Operations” in Part 1.

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The Ultimate Guide to Apache Airflow DAGS

With Airflow being the open-source standard for workflow orchestration, knowing how to write Airflow DAGs has become an essential skill for every data engineer. This eBook provides a comprehensive overview of DAG writing features with plenty of example code. You’ll learn how to: Understand the building blocks DAGs, combine them in complex pipelines, and schedule your DAG to run exactly when you want it to Write DAGs that adapt to your data at runtime and set up alerts and notifications Scale you

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Apache NiFi – the data movement enabler in a hybrid cloud environment

Cloudera

Cloudera provides its customers with a set of consistent solutions running on-premises and in the cloud to ensure customers are successful in their data journey for all of their use cases, regardless of where they are deployed. Cloudera DataFlow provides Apache NiFi in both the Cloudera Data Platform Private Cloud Base (on-premises) and Public Cloud (AWS, Azure, and Google Cloud) products in this hybrid cloud strategy.

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