Sat.Nov 20, 2021 - Fri.Nov 26, 2021

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Azure Data Factory: Fail Activity

Azure Data Engineering

During some scenarios in Azure Data Factory, we may want to intentionally stop the execution of the pipeline. An example could be when we want to check the existence of a file or folder using Get Metadata activity. We may want to fail the pipeline if the file/folder does not exist. To achieve this, we could use the Fail Activity. Invoking the Fail Activity ensures that the pipeline execution will be stopped.

Metadata 130
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Top Stories, Nov 15-21: 19 Data Science Project Ideas for Beginners

KDnuggets

Also: How I Redesigned over 100 ETL into ELT Data Pipelines; Where NLP is heading; Don’t Waste Time Building Your Data Science Network; Data Scientists: How to Sell Your Project and Yourself.

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Laying The Foundation Of Your Data Platform For The Era Of Big Complexity With Dagster

Data Engineering Podcast

Summary The technology for scaling storage and processing of data has gone through massive evolution over the past decade, leaving us with the ability to work with massive datasets at the cost of massive complexity. Nick Schrock created the Dagster framework to help tame that complexity and scale the organizational capacity for working with data. In this episode he shares the journey that he and his team at Elementl have taken to understand the state of the ecosystem and how they can provide a f

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Ten Things I’ve Learned in 20 Years in Data and Analytics

Teradata

Teradata's Martin Willcox recently passed 17 years at Teradata and a quarter of a century in the industry. Here are the ten things he's learned about data analytics in those 20-odd years.

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15 Modern Use Cases for Enterprise Business Intelligence

Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?

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In AI we Trust? Why we Need to Talk about Ethics and Governance (part 1 of 2)

Cloudera

Advances in the performance and capability of Artificial Intelligence (AI) algorithms has led to a significant increase in adoption in recent years. In a February 2021 report by IDC, they estimate that world-wide revenues from AI will grow by 16.4% in 2021 to USD $327 billion. Furthermore, AI adoption is becoming increasingly widespread and not just concentrated within a small number of organisations.

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Most Common SQL Mistakes on Data Science Interviews

KDnuggets

Sure, we all make mistakes -- which can be a bit more painful when we are trying to get hired -- so check out these typical errors applicants make while answering SQL questions during data science interviews.

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More Trending

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How To Succeed As a DataOps Engineer

DataKitchen

What makes an effective DataOps Engineer? A DataOps Engineer shepherds process flows across complex corporate structures. Organizations have changed significantly over the last number of years and even more dramatically over the previous 12 months, with the sharp increase in remote work. A DataOps engineer runs toward errors. You might ask what that means.

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Getting Started with Cloudera Data Platform Operational Database (COD)

Cloudera

Concepts. What is Cloudera Operational Database (COD)? Operational Database is a relational and non-relational database built on Apache HBase and is designed to support OLTP applications, which use big data. The operational database in Cloudera Data Platform has the following components: . Apache Phoenix provides a relational model facilitating massive scalability.

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Top 4 Data Integration Tools for Modern Enterprises

KDnuggets

Maintaining a centralized data repository can simplify your business intelligence initiatives. Here are four data integration tools that can make data more valuable for modern enterprises.

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Data Virtualization: Process, Components, Benefits, and Available Tools

AltexSoft

Nowadays, all organizations need real-time data to make instant business decisions and bring value to their customers faster. But this data is all over the place: It lives in the cloud, on social media platforms, in operational systems, and on websites, to name a few. Not to mention that additional sources are constantly being added through new initiatives like big data analytics , cloud-first, and legacy app modernization.

Process 69
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Prepare Now: 2025s Must-Know Trends For Product And Data Leaders

Speaker: Jay Allardyce, Deepak Vittal, and Terrence Sheflin

As we look ahead to 2025, business intelligence and data analytics are set to play pivotal roles in shaping success. Organizations are already starting to face a host of transformative trends as the year comes to a close, including the integration of AI in data analytics, an increased emphasis on real-time data insights, and the growing importance of user experience in BI solutions.

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Ten Things I’ve Learned in 20 Years in Data and Analytics

Teradata

Teradata's Martin Willcox recently passed 17 years at Teradata and a quarter of a century in the industry. Here are the ten things he's learned about data analytics in those 20-odd years.

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Empowering Digital Innovation Through Data and the Public Cloud Together with Amazon Web Services

Cloudera

As data continues to grow at an exponential rate, our customers are increasingly looking to advance and scale operations through digital transformation and the cloud. These modern digital businesses are also dealing with unprecedented rates of data volume, which is exploding from terabytes to petabytes and even exabytes which could prove difficult to manage.

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Dask DataFrame is not Pandas

KDnuggets

This article is the second article of an ongoing series on using Dask in practice. Each article in this series will be simple enough for beginners, but provide useful tips for real work. The next article in the series is about parallelizing for loops, and other embarrassingly parallel operations with dask.delayed.

Python 155
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Machine Learning NLP Text Classification Algorithms and Models

ProjectPro

Although businesses have an inclination towards structured data for insight generation and decision-making, text data is one of the vital information generated from digital platforms. However, it is not straightforward to extract or derive insights from a colossal amount of text data. To mitigate this challenge, organizations are now leveraging natural language processing and machine learning techniques to extract meaningful insights from unstructured text data.

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How to Drive Cost Savings, Efficiency Gains, and Sustainability Wins with MES

Speaker: Nikhil Joshi, Founder & President of Snic Solutions

Is your manufacturing operation reaching its efficiency potential? A Manufacturing Execution System (MES) could be the game-changer, helping you reduce waste, cut costs, and lower your carbon footprint. Join Nikhil Joshi, Founder & President of Snic Solutions, in this value-packed webinar as he breaks down how MES can drive operational excellence and sustainability.

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Skills Gap in Data Engineering

Pipeline Data Engineering

Most data professionals realise very early in their journey that accessing the knowledge that they really need to solve data engineering problems is hard to come by. The other thing they don’t necessarily see is how short-sighted a lot of courses are, and how most of the technical content they provide is going to be rendered useless in a year or two.

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How Cloudera Is Opening Doors for Underserved Youth

Cloudera

For underserved youth, the lack of educational opportunity can seriously hinder their development and future career prospects. Many are deprived of early childhood chances at experiencing the professional world, so a career in science, finance, IT, or marketing is a pipe dream. . Unless someone shows them it’s possible. At the Middle Tennessee and Peninsula chapters of the Boys & Girls Clubs, high school students are receiving an introduction into a new world of possibilities.

Finance 75
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Accelerating AI with MLOps

KDnuggets

Companies are racing to use AI, but despite its vast potential, most AI projects fail. Examining and resolving operational issues upfront can help AI initiatives reach their full potential.

Project 151
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Is Data Science Hard to Learn? (Answer: NO!)

ProjectPro

“Is data science hard to learn?”, “Is data science a hard job?”, “Is it hard to get a data science job?” Are you a data science enthusiast who believes data science is hard and keeps thinking about such questions? Allow us to challenge your thoughts and read this blog as we will help you answer all those questions.

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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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Building a Metrics Dashboard with Superset and Cube

Preset

In this tutorial, we'll learn how to build a metrics dashboard with Apache Superset, a modern and open-source data exploration and visualization platform. We'll also use Cube, an open-source metrics store, as the data source for Superset.

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RudderStack Product News Vol. #017 - High-performance JavaScript SDK

RudderStack

In this update, we cover our new high-performance JavaScript SDK, announce a new destination integration, and highlight our Event Stream pricing promotion.

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On-Device Deep Learning: PyTorch Mobile and TensorFlow Lite

KDnuggets

PyTorch and TensorFlow are the two leading AI/ML Frameworks. In this article, we take a look at their on-device counterparts PyTorch Mobile and TensorFlow Lite and examine them more deeply from the perspective of someone who wishes to develop and deploy models for use on mobile platforms.

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15 Python Reinforcement Learning Project Ideas for Beginners

ProjectPro

Towards the end of the 2000s, complex neural networks and model-based deep learning saw a huge upsurge in demand with revolutionary results in the fields of computer vision and natural language processing. While reinforcement learning has been around the corner from the same time, it was overshadowed by its counterparts for decades. It first became the talk of the town when in 2016, Google Deepmind’s AlphaGo defeated the World Champion in the Chinese game of Go.

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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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Comprehensive Tutorial for Contributing Code to Apache Superset

Preset

This tutorial post will cover all of the steps needed to make your first code contribution to the Apache Superset project.

Coding 52
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Akka Streams Backpressure Explained

Rock the JVM

Discover how Akka Streams implements backpressure, a key component of the Reactive Streams specification, in this detailed demonstration

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A Spreadsheet that Generates Python: The Mito JupyterLab Extension

KDnuggets

You can call Mito into your Jupyter Environment and each edit you make will generate the equivalent Python in the code cell below.

Python 139
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50 PySpark Interview Questions and Answers For 2023

ProjectPro

PySpark has exploded in popularity in recent years, and many businesses are capitalizing on its advantages by producing plenty of employment opportunities for PySpark professionals. According to the Businesswire report , the worldwide big data as a service market is estimated to grow at a CAGR of 36.9% from 2019 to 2026, reaching $61.42 billion by 2026.

Hadoop 52
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Business Intelligence 101: How To Make The Best Solution Decision For Your Organization

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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Addressing the Three Scalability Challenges in Modern Data Platforms

Cloudera

Introduction. In legacy analytical systems such as enterprise data warehouses, the scalability challenges of a system were primarily associated with computational scalability, i.e., the ability of a data platform to handle larger volumes of data in an agile and cost-efficient way. Open source frameworks such as Apache Impala, Apache Hive and Apache Spark offer a highly scalable programming model that is capable of processing massive volumes of structured and unstructured data by means of paralle

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Comparing Rockset, Apache Druid and ClickHouse for Real-Time Analytics

Rockset

We built Rockset with the mission to make real-time analytics easy and affordable in the cloud. We put our users first and obsess about helping our users achieve speed, scale and simplicity in their modern real-time data stack (some of which I discuss in depth below). But we, as a team, still take performance benchmarks seriously. Because they help us communicate that performance is one of the core product values at Rockset.

MongoDB 59
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5 Tips to Get Your First Data Scientist Job

KDnuggets

Read some of the key things the author has learned during the infamous job seeking stage.

Data 159
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What’s the difference between a Data Scientist and a Data Analyst?

KDnuggets

Find out the major differences between a Data Analyst and a Data Scientist, and read the author's pointers on what they would recommend you to do if you wish to make that transition from Data Analyst to Data Scientist.

Data 117
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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.