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How to build a data project with step-by-step instructions

Start Data Engineering

Identify what tool to use to process data 3.3. Define what the output dataset will look like 3.1.3. Define SLAs so stakeholders know what to expect 3.1.4. Define checks to ensure the output dataset is usable 3.2. Data flow architecture 3.

Project 240
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Reducing The Barrier To Entry For Building Stream Processing Applications With Decodable

Data Engineering Podcast

Summary Building streaming applications has gotten substantially easier over the past several years. Despite this, it is still operationally challenging to deploy and maintain your own stream processing infrastructure. What was the process for adding full Java support in addition to SQL?

Process 182
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An educational side project

The Pragmatic Engineer

I’d like to share a story about an educational side project which could prove fruitful for a software engineer who’s seeking a new job. Juraj created a systems design explainer on how he built this project, and the technologies used: The systems design diagram for the Rides application The app uses: Node.js

Education 363
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Revolutionizing Build Analytics: How to enhance build processes with ThoughtSpot

ThoughtSpot

In the fast-paced world of software development, the efficiency of build processes plays a crucial role in maintaining productivity and code quality. At ThoughtSpot , while Gradle has been effective, the growing complexity of our projects demanded a more sophisticated approach to understanding and optimizing our builds.

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How to Find and Test Assumptions in Product Development

Assumptions mapping is the process of identifying and testing your riskiest ideas. Watch this webinar with Laura Klein, product manager and author of Build Better Products, to learn how to spot the unconscious assumptions which you’re basing decisions on and guidelines for validating (or invalidating) your ideas.

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Building cost effective data pipelines with Python & DuckDB

Start Data Engineering

Project demo 3. Building efficient data pipelines with DuckDB 4.1. Use DuckDB to process data, not for multiple users to access data 4.2. Cost calculation: DuckDB + Ephemeral VMs = dirt cheap data processing 4.3. Processing data less than 100GB? Introduction 2. Use DuckDB 4.4.

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Kafka to MongoDB: Building a Streamlined Data Pipeline

Analytics Vidhya

Introduction Data is fuel for the IT industry and the Data Science Project in today’s online world. Handling and processing the streaming data is the hardest work for Data Analysis. IT industries rely heavily on real-time insights derived from streaming data sources.

MongoDB 217
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New Study: 2018 State of Embedded Analytics Report

Why do some embedded analytics projects succeed while others fail? We surveyed 500+ application teams embedding analytics to find out which analytics features actually move the needle. Read the 6th annual State of Embedded Analytics Report to discover new best practices. Brought to you by Logi Analytics.