Wed.Sep 04, 2024

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What are the Key Parts of Data Engineering?

Start Data Engineering

1. Introduction 2. Key parts of data systems: 2.1. Requirements 2.2. Data flow design 2.3. Orchestrator and scheduler 2.4. Data processing design 2.5. Code organization 2.6. Data storage design 2.7. Monitoring & Alerting 2.9. Infrastructure 3. Conclusion 1. Introduction If you are trying to break into (or land a new) data engineering job, you will inevitably encounter a slew of data engineering tools.

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Streaming Postgres data to Databricks Delta Lake in Unity Catalog

Confessions of a Data Guy

Over the many years I’ve been pounding my keyboard … Perl, PHP, Python, C#, Rust … whatever … I, like most programmers, built up a certain disdain for what is called Low Code / No Code solutions. In my rush to worship at the feet of the code we create, I failed, in the beginning, […] The post Streaming Postgres data to Databricks Delta Lake in Unity Catalog appeared first on Confessions of a Data Guy.

Python 100
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Read Meta’s 2024 Sustainability Report

Engineering at Meta

We are working in partnership with others to scale inclusive solutions that support the transition to a zero-carbon economy and help create a healthier planet for all.

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Introduction to Polars in 2 Minutes

Confessions of a Data Guy

Polars is the hot new Rust based Python Dataframe tool that is taking over the world and destryoing Pandas even as we speak. You want the quick and dirty introduction to Polars? Look no farther. The post Introduction to Polars in 2 Minutes appeared first on Confessions of a Data Guy.

Python 100
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Improving the Accuracy of Generative AI Systems: A Structured Approach

Speaker: Anindo Banerjea, CTO at Civio & Tony Karrer, CTO at Aggregage

When developing a Gen AI application, one of the most significant challenges is improving accuracy. This can be especially difficult when working with a large data corpus, and as the complexity of the task increases. The number of use cases/corner cases that the system is expected to handle essentially explodes. 💥 Anindo Banerjea is here to showcase his significant experience building AI/ML SaaS applications as he walks us through the current problems his company, Civio, is solving.

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Enhanced Workflows UI reduces debugging time and boosts productivity

databricks

Data teams spend way too much time troubleshooting issues, applying patches, and restarting failed workloads. It's not uncommon for engineers to spend their.

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Comprehensive Guide to Modern Data Warehouse in 2024

Hevo

A data warehouse is a centralized system that stores, integrates, and analyzes large volumes of structured data from various sources. It is predicted that more than 200 zettabytes of data will be stored in the global cloud by 2025.

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Precisely Women in Technology: Meet Mahima

Precisely

According to the Women in Tech Network , women make up about 35 percent of the tech workforce. While this number has grown over the years, it still indicates that technology is a male-dominated industry. Precisely is committed to creating a supportive environment for women to build their careers so that this number can continue growing. As a result, the Precisely Women in Technology (PWIT) network was developed.

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Let Flink Cook: Mastering Real-Time Retrieval-Augmented Generation (RAG) with Flink

Confluent

How to use Flink AI model inference with familiar SQL syntax to work directly with LLMs and vector databases for your generative AI use cases.

SQL 69
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Your Guide to Building the Perfect Data Quality Dashboard

Monte Carlo

Picture this: You’re leading a meeting, ready to present the latest sales figures. But, as you start sharing the numbers, someone points out a glaring inconsistency. Suddenly, the room is filled with doubt—about the data, the insights, and, let’s face it, even your judgment. A data quality dashboard is your safety net in these situations. It’s more than a tool—it’s a real-time report card on the health of your data.

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Ghosted After an Interview? 5 Resources to Help You Bounce Back

KDnuggets

Check out this list of resources for different types of interviews.

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The Ultimate Guide To Data-Driven Construction: Optimize Projects, Reduce Risks, & Boost Innovation

Speaker: Donna Laquidara-Carr, PhD, LEED AP, Industry Insights Research Director at Dodge Construction Network

In today’s construction market, owners, construction managers, and contractors must navigate increasing challenges, from cost management to project delays. Fortunately, digital tools now offer valuable insights to help mitigate these risks. However, the sheer volume of tools and the complexity of leveraging their data effectively can be daunting. That’s where data-driven construction comes in.

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Application Development vs. Web Development: A Simple Guide

Edureka

Deciding which path to follow between Application Development vs Web Development is a decision that should not be taken lightly. They are both relevant and promising. In both areas, there are numerous opportunities. This guide will help to introduce each one, to state what it is that they do, the skills required, and how they may differ in terms someone with little or no background in sociology can understand.

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5 Must-Know R Packages for Data Analysis

KDnuggets

Here are five must-know R packages for data analysis in R.

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Top dbt Alternatives and Competitors –  Ranked by G2

Hevo

In this fast-changing world of data analytics, choosing the right tool for data transformation is one of the keys. Grown in this sector, dbt, or what is popularly known as the data build tool, is a significant solution for SQL-based data transformations, keeping workflows properly and well-documented by data teams inside data warehouses.

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How to Implement Complex Filters on DataFrame Columns with Pandas

KDnuggets

Learn how to acquire data you need with Pandas filter syntax.

Data 64
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Building Your BI Strategy: How to Choose a Solution That Scales and Delivers

Speaker: Evelyn Chou

Choosing the right business intelligence (BI) platform can feel like navigating a maze of features, promises, and technical jargon. With so many options available, how can you ensure you’re making the right decision for your organization’s unique needs? 🤔 This webinar brings together expert insights to break down the complexities of BI solution vetting.

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Data Lake vs Data Warehouse: How to choose?

Hevo

Currently, data management is a continually developing field that requires careful consideration when deciding which solution should be implemented to store, process, and analyze data effectively. There are two forms that are frequently selected: data warehouse vs data lake.

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Detecting AI-written code: lessons on the importance of data quality by Amy Laws

Scott Logic

Our team had previously built a tool to investigate code quality from PR data. Building on this work, we set about finding a method to detect AI-written code, so we could investigate any potential differences in code quality between human and AI-written code. During our time on this project, we learnt some important lessons, including just how hard it can be to detect AI-written code, and the importance of good-quality data when conducting research.

Coding 72
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Alteryx vs Matillion: A Side-by-Side Detailed Comparison

Hevo

Data is the new currency in today’s world, helping industries make decisions and innovations. To use data to its full potential, organizations require powerful tools to manage, transform, and analyze vast amounts of it. Various tools are available, among which Alteryx and Matillion stand out as two of the leading ETL solutions.

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What is ThoughtSpot? Everything You Need to Know

phData: Data Engineering

This article was co-written by Lynda Chao & Tess Newkold With the growing interest in AI-powered analytics, ThoughtSpot stands out as a leader among legacy BI solutions known for its self-service search-driven analytics capabilities. ThoughtSpot offers AI-powered and lightning-fast analytics, a user-friendly semantic engine that is easy to learn, and the ability to empower users across any organization to quickly search and answer data questions.

BI 52
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Driving Responsible Innovation: How to Navigate AI Governance & Data Privacy

Speaker: Aindra Misra, Senior Manager, Product Management (Data, ML, and Cloud Infrastructure) at BILL

Join us for an insightful webinar that explores the critical intersection of data privacy and AI governance. In today’s rapidly evolving tech landscape, building robust governance frameworks is essential to fostering innovation while staying compliant with regulations. Our expert speaker, Aindra Misra, will guide you through best practices for ensuring data protection while leveraging AI capabilities.

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Harnessing Continuous Data Streams: Unlocking the Potential of Online Machine Learning

Striim

The world is generating an astonishing amount of data every second of every day. It reached 64.2 zettabytes in 2020, and is projected to mushroom to over 180 zettabytes by 2025, according to Statista. Modern problems require modern solutions — which is why businesses across industries are moving away from batch processing and towards real-time data streams, or streaming data.