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Confessions of a Data Guy
MAY 30, 2024
Build new pipeline, update pipeline, new data model, fix bug, etc, etc. It’s a constant stream of data, new and old, spilling into our Data Warehouses and […] The post Building Data Platforms (from scratch) appeared first on Confessions of a Data Guy. It’s never-ending.
Data Engineering Podcast
MAY 18, 2024
The team at Zenlytic have leaned on the promise of large language models to build an AI agent that lets you converse with your data. Are data agents harder to build? What are the most interesting, unexpected, or challenging lessons that you have learned while working on building an AI agent for business intelligence?
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Start Data Engineering
MAY 28, 2024
Building efficient data pipelines with DuckDB 4.1. Introduction 2. Project demo 3. 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? Use DuckDB 4.4.
Analytics Vidhya
FEBRUARY 28, 2024
We know that streaming data is data that is emitted at high volume […] The post Kafka to MongoDB: Building a Streamlined Data Pipeline appeared first on Analytics Vidhya. Handling and processing the streaming data is the hardest work for Data Analysis.
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This will help you decide whether to build an in-house entity resolution system or utilize an existing solution like the Senzing® API for entity resolution. This guide will walk you through the requirements and challenges of implementing entity resolution.
Data Engineering Podcast
SEPTEMBER 17, 2023
Summary A significant amount of time in data engineering is dedicated to building connections and semantic meaning around pieces of information. In this episode Brian Platz explains how JSON-LD can be used as a shared representation of linked data for building semantic data products. Hex brings everything together.
Data Engineering Podcast
OCTOBER 1, 2023
Now that AI has reached the level of sophistication seen in the various generative models it is being used to build new ETL workflows. In this episode Jay Mishra shares his experiences and insights building ETL pipelines with the help of generative AI. How can you get the best results for your use case?
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Every time an application team gets caught up in the “build vs buy” debate, it stalls projects and delays time to revenue. Partnering with an analytics development platform gives you the freedom to customize a solution without the risks and long-term costs of building your own. There is a third option.
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Organizational data literacy is regularly addressed, but it’s uncommon for product managers to consider users’ data literacy levels when building products. Product managers need to research and recognize their end users' data literacy when building an application with analytic features.
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Download this eBook to discover insights from 16 top product experts, and learn what it takes to build a successful application with analytics at its core. What should product managers keep in mind when adding an analytics project to their roadmap?
Speaker: Anne Steiner and David Laribee
You’ll walk away with a comprehensive understanding of: The transformative power of strategic DX improvements in fostering a product-oriented strategy, driving up customer satisfaction and revenue 📈 DX’s impact on the entire Product Development Lifecycle (PDLC), with a review of tangible, real-world illustrations 🌐 Actionable strategies (..)
Speaker: Jon Harmer, Product Manager for Google Cloud
Create actual artifacts for your products: With the practical experience provided in this session, apply these tools to real-world product management scenarios to build journey and impact maps for actual users & products.
Speaker: Ryan MacCarrigan, Founding Principal, LeanStudio
Many product teams use charting components and open source code libraries to get dashboards and reporting functionality quickly. But what happens when you have a growing user base and additional feature requests?
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The Definitive Guide to Predictive Analytics has everything you need to get started, including real-world examples, steps to build your models, and solutions to common data challenges. What You'll Learn: 7 steps to embed predictive analytics in your application—from identifying a problem to solve to building your prototype.
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To better understand the factors behind the decision to build or buy analytics, insightsoftware partnered with Hanover Research to survey IT, software development, and analytics professionals on why they make the embedded analytics choices they do.
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