Sat.Feb 03, 2024 - Fri.Feb 09, 2024

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Top 5 AI Coding Assistants You Must Try

KDnuggets

Discover the top AI coding assistants that can 10X your productivity overnight - #5 has the best autocomplete feature, and #1 is the most advanced code assistant tool ever seen!

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Table file formats - streaming writer: Delta Lake

Waitingforcode

The previous blog from the series we discovered streaming reader. However, an end-to-end streaming Delta Lake pipeline also requires a writer which will be our focus today.

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Data News — Week 24.05

Christophe Blefari

hey ( credits ) Hello here, this is Christophe from Amsterdam. I hope you're doing good. I'm in Amsterdam for the day for the DuckCon #4. The DuckDB annual conference, and god I like Europe. Being able to travel by train from Berlin to Paris to Amsterdam while going to the west of France for a lecture in a week is something truly awesome. Anyway this week will be a mixed Data News with links, stuff and ideas and a small wrap-up of the DuckCon + the stuff I presented on Wed. to a Modern

MongoDB 130
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Unapologetically Technical Episode 8 – Tom Scott

Jesse Anderson

It has been quite a while, but we’re finally back to a new episode this year! In this episode of Unapologetically Technical, I interview Tom Scott, the Founder and CEO of Streambased. Join us as we talk about distributed systems and how he created distributed or what we call the Monte Carlo simulations. We also talk about his work across various companies like how he created and ran a data warehouse at Sky Betting, his work at Cloudera doing Customer Operations Engineering, and how that he

Kafka 100
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Navigating the Future: Generative AI, Application Analytics, and Data

Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?

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University of Cincinnati MS Business Analytics Summer 2024 Information Session

KDnuggets

Don't miss this chance to chart your course toward a successful career in business analytics. Reserve your spot now and embark on a journey of knowledge and growth!

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IoT Data Streaming for Building Private Wireless Networks

Confluent

Confluent enables real-time, reliable, scalable, and secure communication between IoT devices, applications, and backend systems. Streamline data processing and unlock analytics to boost productivity and time to market while lowering infrastructure costs.

Building 116

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Health Care Outside of the Box

Cloudera

How enterprise-grade data management creates better and more efficient care. In the last few years, the acceptance of telehealth has become more widespread as patients and providers found they could maintain continuity through phone and video collaboration, instead of in-person visits. In many cases, a level of care that once required a drive to the clinic or hospital could be delivered over a mobile phone or laptop, with no travel and no waiting room.

Medical 98
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Breaking Down DENSE_RANK(): A Step-by-Step Guide for SQL Enthusiasts

KDnuggets

This article introduced you to the world of ranking functions in SQL. We will cover the basics of how they work, how they're used, and how to avoid common pitfalls.

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Welcome Noteable: Making Data Streaming Easier and More Approachable

Confluent

Confluent has hired many Noteable employees to help make application development easier for both Kafka and Flink developers.

Kafka 125
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From Cloud-native to Hybrid and back again

Picnic Engineering

From Cloud-native to Hybrid and back again: Picnic’s on-premises computing journey Many companies are working on their digital transformation, transitioning their traditional on-premises deployment to a cloud setup. Other companies, such as Picnic, have started in the cloud and are running a modern cloud native tech stack from the outset. Picnic’s infrastructure design focuses on a rapidly scalable cloud solution.

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Get Better Network Graphs & Save Analysts Time

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.

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Infographic design in Business Analyst: Best practices for layers and display modes

ArcGIS

Best practices for using layers and different display modes in Infographic templates in ArcGIS Business Analyst and Community Analyst

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5 Free Courses to Master Python for Data Science

KDnuggets

Want to learn Python to kickstart your career in data? Here are five free courses to help you master Python for data science.

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Top 5 Data + AI Predictions for Financial Services in 2024

Snowflake

Generative AI tops every list of major financial services trends for 2024. And it’s no wonder — this new technology has the potential to revolutionize the industry by augmenting the value of employee work, driving organizational efficiencies, providing personalized customer experiences, and uncovering new insights from vast amounts of data. Its predictive capabilities can help leaders anticipate market trends and make more informed decisions, improving financial outcomes for customers as well as

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A Data Mesh Implementation: Expediting Value Extraction from ERP/CRM Systems

Towards Data Science

Enabling fast data development from big operational systems Photo by Benjamin Zanatta on Unsplash The challenge when facing the ‘monster’ For a data engineer building analytics from transactional systems such as ERP (enterprise resource planning) and CRM (customer relationship management), the main challenge lies in navigating the gap between raw operational data and domain knowledge.

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Understanding User Needs and Satisfying Them

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.

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US Air Force Hackathon: How Large Language Models Will Revolutionize USAF Flight Test

databricks

What is the US Air Force (USAF) Hackathon? The Air Force Test Center (AFTC) Data Hackathon is a consortium of test experts across.

Data 97
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Navigating Today’s Data and AI Market Uncertainty

KDnuggets

It’s more important than ever to think long-term about the analytics partnerships you forge. Are you choosing technologies that will stand the test of time? Are you choosing companies with proven track records?

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Snowflake Improves Query Duration by 20% on Stable Workloads Since We Began Tracking the Snowflake Performance Index

Snowflake

Earlier this year at Snowflake Summit, we announced the public launch of the Snowflake Performance Index (SPI), an aggregate index for measuring real-world improvements in Snowflake performance experienced by customers over time. In this post, we provide our biannual update to showcase the latest improvements. The Snowflake performance philosophy Our product philosophy revolves around a continuous quest to enhance Snowflake performance, with a particular focus on refining the core database engin

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How to Use Confluent for Kubernetes to Manage Resources Outside of Kubernetes

Confluent

Learn how to implement GitOps for Apache Kafka with Confluent for Kubernetes and Confluent Platform. Automate resource deployment and streamline administration.

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Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

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.

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LIMIT: Less is More for Instruction Tuning

databricks

Pretrained large language models aren’t particularly good at responding in concise, coherent sentences out of the box. At a minimum, they have to b.

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Free Data Science Interview Book to Land Your Dream Job

KDnuggets

Are you preparing for your dream data science job but feeling overwhelmed by the vast amount of online resources? Look no further than this free and easily accessible web-based book to help you brush up on your skills and feel confident for your upcoming interview.

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4 GenAI Opportunities from Real Data Teams

Monte Carlo

The funny thing about hype is that it’s always at its apex when information is at its lowest. And GenAI is no different. A lot of organizations want to talk about AI, but it’s tough to find teams that are actually leveraging it in a meaningful way. (Although, we did create the above image using DALL-E, and I think you could say it’s pretty meaningful.

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Data Engineering Weekly #157

Data Engineering Weekly

RudderStack is the Warehouse Native CDP, built to help data teams deliver value across the entire data activation lifecycle, from collection to unification and activation. Visit rudderstack.com to learn more. Joe Reis: Definition of Data Modeling & What Data Modeling Is not Joe raised a very fundamental question in data engineering. What is Data Modeling, and what is not?

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How Embedded Analytics Gets You to Market Faster with a SAAS Offering

Start-ups & SMBs launching products quickly must bundle dashboards, reports, & self-service analytics into apps. Customers expect rapid value from your product (time-to-value), data security, and access to advanced capabilities. Traditional Business Intelligence (BI) tools can provide valuable data analysis capabilities, but they have a barrier to entry that can stop small and midsize businesses from capitalizing on them.

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Data Model Design 101: Composite vs Surrogate Keys

Towards Data Science

When to know which type of key to use in your data models Continue reading on Towards Data Science »

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Books, Courses, and Live Events to Learn Generative AI with O’Reilly

KDnuggets

If you are new to generative AI or an expert who wants to learn more, O’Reilly offers a range of resources to kickstart your generative AI journey.

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Evaluating Retrieval in RAGs: A Gentle Introduction

Tweag

No, not this RAG. Despite their many capabilities, Large Language Models (LLMs) have a serious limitation: they’re stuck in time and their knowledge is limited to the data they have been trained on. Updating the knowledge of an LLM can take two forms: fine-tuning, which we will address in a future post, and the ever-present RAG. RAG, short for Retrieval Augmented Generation, has garnered a lot of attention in the GenAI community and for good reasons.

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Maximizing the Value of Your Address Data with Geo Addressing

Precisely

Address data: for many businesses, it falls into one of two categories – a valuable asset, or a major (and costly) headache. What makes address data so challenging for those that fall into the latter category? Well, address challenges can often be traced to human error. We may be constantly glued to our computers and phones, but typos are still inevitable.

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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

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.

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Will GenAI Replace Data Engineers? No — And Here’s Why. 

Monte Carlo

These days, keeping up with the latest advancements in GenAI is harder than saying “multimodal model.” It seems like every week some shiny new solution launches with the lofty promise of transforming our lives, our work, and the way we feed our dogs. Data engineering is no exception. Already in the wee months of 2024, GenAI is beginning to upend the way data teams think about ingesting, transforming, and surfacing data to consumers.

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Sentiment Analysis in Python: Going Beyond Bag of Words

KDnuggets

This code based tutorial provides a brief introduction to Sentiment Analysis, a method used to predict emotions, similar to a digital psychologist.

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Connect With Confluent Expands to 40+ Connections With Q1 Entrants

Confluent

Confluent’s data streaming ecosystem expands and highlights customer success driven by technology partners.

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Precisely Women in Technology: Meet Elizabeth

Precisely

The Precisely Women in Technology (PWIT) program was first established to create a space for women in the organization to come together. As the program has evolved throughout the years, more and more resources have become available to women such as mentorship opportunities on both sides; fireside chats with leaders; a book club; and, much more. Each month, a member from the network is selected to participate in a Q&A to share more about her experience as a woman in technology.

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From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

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.