Wed.Nov 30, 2022

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Data Science Projects That Can Help You Solve Real World Problems

KDnuggets

The best way to learn Data Science is by solving real-world problems with the data and building your own portfolio. In this article, we will discuss three projects that you can work on to build your portfolio and impress interviewers.

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Stream Processing, CEP, Event Sourcing, and Data Streaming Explained

Confluent

What is stream processing, or complex event processing (CEP), and how does it work? Learn about real-time data and event stream analytics in this tutorial.

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8 Best Python Image Manipulation Tools

KDnuggets

Want to extract underlying data from images? This article lists some of the best Python image manipulation tools that help you transform images.

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Teradata Recognized as a Designated Member of the Amazon SageMaker Ready Program

Teradata

Teradata has joined the Amazon SageMaker Ready Program which differentiates Teradata as an AWS Partner Network member with a product that works with Amazon SageMaker & fully supports AWS customers.

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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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Transaction Support in Cloudera Operational Database (COD)

Cloudera

What is CDP Operational Database (COD). CDP Operational Database enables developers to quickly build future-proof applications that are architected to handle data evolution. It helps developers automate and simplify database management with capabilities like auto-scale, and is fully integrated with Cloudera Data Platform (CDP). For more information and to get started with COD, refer to Getting Started with Cloudera Data Platform Operational Database (COD).

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Putting Apache Kafka To Use: A Practical Guide to Building an Event Streaming Platform (Part 1)

Confluent

Putting Apache Kafka To Use: A Practical Guide to Building an Event Streaming Platform.

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An introduction to Markdown by Charlie Olive

Scott Logic

An introduction to Markdown Markdown is a brilliant tool for quickly writing up universally accessible documents. Created by John Gruber and Aaron Schwartz in 2004, it stands as one of the most popular and widely used markup languages around. It uses simple and intuitive formatting that can be easily read and understood. “A Markdown-formatted document should be publishable as-is, as plain text, without looking like it’s been marked up with tags or formatting instructions” John Gruber, creator of

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Higher-orderness is first-order interaction

Tweag

There is an inherent beauty to be found in simple, pervasive ideas that shift our perspective on familiar objects. Such ideas can help tame the complexity of abstruse abstractions by offering a more intuitive angle from which to understand them. The aim of this post is to present an alternative angle — that of interactive semantics — from which to view one of the fundamental notion of functional programming: higher-order functions.

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Striim Cloud on AWS: Unify your data with a fully managed change data capture and data streaming service

Striim

Businesses of all scales and industries have access to increasingly large amounts of data, which need to be harnessed effectively. According to an IDG Market Pulse survey , companies collect data from 400 sources on average. Companies that can’t process and analyze it to glean useful insights for their operations are falling behind. Thousands of companies are centralizing their analytics and applications on the AWS ecosystem.

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Putting Apache Kafka To Use: A Practical Guide to Building an Event Streaming Platform (Part 2)

Confluent

This is the second part of our guide on streaming data and Apache Kafka. This guide will contain specific advice on how to go about building an event streaming platform in your organization.

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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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Rules help you go faster by Jessica McEvoy

Scott Logic

Over the summer, in partnership with Scott Logic, the Institute for Government (IfG) ran a series of roundtable discussions with senior civil servants and government experts on the topic of Data Sharing in Government. This is the second in a series of blog posts in which I reflect on the key themes that came out of those discussions. You can read the first post in the series here on ‘ Why you should get the right people in the room from the start ’.

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Diagnose and Debug Apache Kafka Issues: Understanding Increased Connections

Confluent

Given their nature, broker connections can be tough to understand and keep track of, but that doesn’t mean that you can’t have control of your Kafka cluster!

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How to Deploy Transaction Support on Cloudera Operational Database (COD)

Cloudera

What is Cloudera Operational Database (COD). Cloudera Operational Database enables developers to quickly build future-proof applications that are architected to handle data evolution. It helps developers automate and simplify database management with capabilities like auto-scale, and is fully integrated with Cloudera Data Platform (CDP). For more information and to get started with COD, refer to our article Getting Started with Cloudera Data Platform Operational Database (COD).

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How To Implement Data Mesh: Top Tips From 4 Data Leaders

Monte Carlo

Data leaders across industries are embracing data mesh. It’s easy to be skeptical based on past trends that have come and gone. Those fads forced us to adjust our strategy, overhaul our tech or re-skill our teams. But the reason data teams want to better understand how to implement data mesh is because it solves genuine pain-points. Specifically, the problems created by an exhaust-friendly data lake and the all-too-often disconnect between teams – of data producers, data consumers and those in b

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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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Enabling static analysis of SQL queries at Meta

Engineering at Meta

UPM is our internal standalone library to perform static analysis of SQL code and enhance SQL authoring. UPM takes SQL code as input and represents it as a data structure called a semantic tree. Infrastructure teams at Meta leverage UPM to build SQL linters, catch user mistakes in SQL code, and perform data lineage analysis at scale. Executing SQL queries against our data warehouse is important to the workflows of many engineers and data scientists at Meta for analytics and monitoring use cases

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The Ravit Show Q&A: How More Data Observability Leads to Better Governance

Databand.ai

The Ravit Show Q&A: How More Data Observability Leads to Better Governance Ryan Yackel 2022-11-30 10:18:32 We recently had the opportunity to join an episode of The Ravit Show , a community for data science and AI professionals to upskill, grow, share, and learn from each other. Ryan Yackel, Product Evangelist at Databand, and Kip Yego, Program Director at IBM, joined Ravit Jain to talk about all things data observability and data governance.

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How to Democratize AI/ML and Data Science with AI-generated Synthetic Data

KDnuggets

Synthetic data generation is a solution that allows citizen data scientists and auto ML users to quickly and safely create and use business-critical data assets. Benefits go beyond democratizing data access, and even those with privileged data access build synthetic data generators into their workflows.