Confessions of a Data Guy
APRIL 28, 2024
A few years ago I wasn’t sure, who was going to win, Golang seemed to be popular, and still is for that matter. When I first wrote a little Golang (~2+ years ago) I was just trying to see what the hype was all about. The funny thing is, at the time, and today, it […] The post Why did Golang lose to Rust for Data Engineering? appeared first on Confessions of a Data Guy.
Seattle Data Guy
APRIL 28, 2024
If you’ve worked in data long enough, then you’ve likely come across the term change data capture. Often called CDC, change data capture involves tracking and recording changes in a database as they happen, and then transmitting these changes to designated targets. This can be crucial because some pipelines, in particular batch pipelines, don’t capture… Read more The post Terms You Should Know If You’re Planning To Use Change Data Capture appeared first on Seattle D
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Seattle Data Guy
APRIL 25, 2024
As data increased in volume, velocity, and variety, so, in turn, did the need for tools that could help process and manage those larger data sets coming at us at ever faster speeds. As a result, frameworks such as Apache Spark and Apache Flink became popular due to their abilities to handle big data processing… Read more The post Apache Spark Vs Apache Flink – How To Choose The Right Solution appeared first on Seattle Data Guy.
Waitingforcode
APRIL 24, 2024
As a data engineer you're certainly familiar with data skew. Yes, this bad phenomena where one task takes considerably more input than the others and often causes unexpected latency or failures. Turns out, stream processing also has its skew but more related to time.
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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.
Start Data Engineering
APRIL 22, 2024
1. Introduction 2. Ensure the code’s logic is working as expected with tests 2.1. Test types for data pipelines 2.2. pytest: A powerful Python library for testing 2.2.1. Set context, run code, check results & clean up 2.2.2. Tests are identified by their name 2.2.3. Use fixture to create fake data for testing 2.2.4. Define items to be shared among tests with conftest.
KDnuggets
APRIL 25, 2024
Interested in switching to data engineering? Here’s a list of Python libraries you’ll find super helpful.
Data Engineering Digest brings together the best content for data engineering professionals from the widest variety of industry thought leaders.
databricks
APRIL 24, 2024
Unlock the power of Apache Spark™ with Unity Catalog Lakeguard on Databricks Data Intelligence Platform. Run SQL, Python & Scala workloads with full data governance & cost-efficient multi-user compute.
Start Data Engineering
APRIL 22, 2024
1. Introduction 2. Docker concepts 2.1. Define the OS and its configurations with an image 2.2. Use the image to run containers 2.2.1. Communicate between containers and local OS 2.2.2. Start containers with docker CLI or compose 3. Conclusion 1. Introduction Docker can be overwhelming to start with. Most data projects use Docker to set up the data infra locally (and often in production).
KDnuggets
APRIL 23, 2024
This article introduces retrieval augmented generation, which combines text generation with informaton retrieval in order to improve language model output.
Knowledge Hut
APRIL 26, 2024
Machine Learning is a sub-branch of Artificial Intelligence, used for the analysis of data. It learns from the data that is input and predicts the output from the data rather than being explicitly programmed. Machine Learning is among the fastest evolving trends in the I T industry. It has found tremendous use in sectors across industries, with its ability to solve complex problems which humans are not able to solve using traditional techniques.
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.
Scott Logic
APRIL 22, 2024
Generative AI has been grabbing headlines, but many businesses are starting to feel left-behind. Large-model AI is becoming more and more influential in the market, and with the well-known tech giants starting to introduce easy-access AI stacks, a lot of businesses are left feeling that although there may be a use for AI in their business, they’re unable to see what use cases it might help them with.
databricks
APRIL 23, 2024
We're thrilled to announce the General Availability (GA) of Databricks Asset Bundles (DABs). With DABs you can easily bundle resources like jobs.
ArcGIS
APRIL 23, 2024
Do you have questions about how to access, use, or nominate content within ArcGIS Living Atlas of the World? Check out this blog for answers.
KDnuggets
APRIL 22, 2024
Are you an aspiring data scientist? If so, these free data science courses from Stanford will help you move forward in your data science journey!
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.
Knowledge Hut
APRIL 25, 2024
Big data in information technology is used to improve operations, provide better customer service, develop customized marketing campaigns, and take other actions to increase revenue and profits. In the world of technology, things are always changing. What was once popular and in demand can quickly become outdated. It is especially true in the world of big data.
Cloudera
APRIL 26, 2024
This month is Allyship April—a time dedicated to deepening our understanding of allyship and its profound impact on fostering inclusive cultures. Allyship isn’t merely a buzzword; it’s a fundamental commitment to actively support and advocate for marginalized individuals and communities within our organization. This month, we’ve engaged in meaningful conversations, challenged our assumptions, and committed to tangible actions that drive positive change.
databricks
APRIL 23, 2024
For a limited time, we're offering 50% off training and certification at Data + AI Summit with the following code: TRAIN50FOTY. This offer.
ArcGIS
APRIL 22, 2024
A drawing alert notification system was added in ArcGIS Pro 3.2 as a method for resolving drawing issues in your ArcGIS Pro projects.
Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage
Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.
KDnuggets
APRIL 26, 2024
Find out all about Google Cloud's latest learning path, and learn how to use the Gemini language model in the Google Cloud.
Knowledge Hut
APRIL 26, 2024
An agile framework is an iterative approach toward completing a project or a particular task under it. A framework helps in planning, managing, and executing tasks in a way that ensures successful project delivery. These frameworks are divided into two categories: frameworks that work within the teams and those that work at a larger scale for the entire organization.
Cloudera
APRIL 23, 2024
Back in October, we announced the first-ever Cloudera Climate and Sustainability Hackathon , powered by AMD. The Hackathon was intended to provide data science experts with access to Cloudera machine learning to develop their own Accelerated Machine Learning Project (AMP) focused on solving one of the many environmental challenges facing the world today.
databricks
APRIL 26, 2024
We’re excited to announce the Databricks Generative AI Hackathon winners. This hackathon garnered hundreds of data and AI practitioners spanning 60 invited companies.
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Outdated or absent analytics won’t cut it in today’s data-driven applications – not for your end users, your development team, or your business. That’s what drove the five companies in this e-book to change their approach to analytics. Download this e-book to learn about the unique problems each company faced and how they achieved huge returns beyond expectation by embedding analytics into applications.
Precisely
APRIL 25, 2024
Jean-Paul Otte recently joined Precisely as Head of Data Strategy Services for Europe. His specialty? Data! Jean-Paul sat down for an interview where we discussed his background as a former CDO, the challenges he faced, and how he developed his unique perspective and data governance expertise. Hello Jean-Paul, could you tell us a little about your background?
KDnuggets
APRIL 23, 2024
Looking to level up your Python skills and ace coding interviews? Start practicing today on these platforms.
Knowledge Hut
APRIL 26, 2024
The era of rapid change We are living in an era where change has become the norm rather than an exception. Emerging technologies and market unpredictability have further fueled change, impacting all industries globally. But the true test of an organization's capability is its ability to endure change and adapt to it. This is the philosophy of ‘Kaizen’ or changing for the better, that helps organizations stay competitive, relevant and in focus with the customer.
ArcGIS
APRIL 26, 2024
Water utilities with authoritative data, analytics, and technology solutions are going to successfully navigate improvements to the Lead and Copper Rule.
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Are you trying to decide which entity resolution capabilities you need? It can be confusing to determine which features are most important for your project. And sometimes key features are overlooked. Get the Entity Resolution Evaluation Checklist to make sure you’ve thought of everything to make your project a success! The list was created by Senzing’s team of leading entity resolution experts, based on their real-world experience.
Lyft Engineering
APRIL 24, 2024
Written by Shumpei Goke and Jinshu Niu Why Technical Learning? At Lyft, data scientists tackle challenging technical problems every day. To support and empower our data scientists, Lyft’s Technical Learning Council (TLC) provides diverse and high-quality continuous learning opportunities to hone their technical skills. TLC’s mission is “ to equip Data Science team members with the technical knowledge and skills that are applicable to their work and helpful to their career advancement.
Hevo
APRIL 26, 2024
Your organization may choose Microsoft SQL Server (MSSQL) on AWS RDS to store its operational data because there are no upfront investments. With AWS RDS MSSQL, you only need to pay for what your organization utilizes. In today’s dynamic business world, achieving the maximum value from your data is crucial.
KDnuggets
APRIL 25, 2024
List of top MLOPs platforms that will help you with integration, training, tracking, deployment, monitoring, CI/CD, and optimizing the infrastructure.
Knowledge Hut
APRIL 26, 2024
Datasets are the repository of information that is required to solve a particular type of problem. Also called data storage areas , they help users to understand the essential insights about the information they represent. Datasets play a crucial role and are at the heart of all Machine Learning models. Machine Learning without data sets will not exist because ML depends on data sets to bring out relevant insights and solve real-world problems.
Speaker: Anne Steiner and David Laribee
As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineers’ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.
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