Data Science Blog: Data Engineering

NOVEMBER 15, 2023

In addition to Business Intelligence (BI), Process Mining is no longer a new phenomenon, but almost all larger companies are conducting this data-driven process analysis in their organization. This aspect can be applied well to Process Mining, hand in hand with BI and AI.

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Robinhood

FEBRUARY 7, 2024

from Robinhood Data Infrastructure Robinhood adheres to a data-first philosophy. Every decision we make here (or every decision at the company), from feature rollouts to operational changes, is backed by data. When dealing with large-scale data, we turn to batch processing with distributed systems to complete high-volume jobs.

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Edureka

AUGUST 2, 2023

Organisations and businesses are flooded with enormous amounts of data in the digital era. Raw data, however, is frequently disorganised, unstructured, and challenging to work with directly. Data processing analysts can be useful in this situation. What Does a Data Processing Analyst Do?

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Data Process Process Data Cleanse Data Mining 52

Knowledge Hut

FEBRUARY 29, 2024

Being a data scientist means constantly growing, enabling businesses to become more data-propelled, and learning newer trends and tools. There are various excellent resources in data science that can help you to develop your skillset. So, having the right knowledge of tools and technology is important for handling such data.

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Knowledge Hut

JULY 28, 2023

Data Labeling is the process of assigning meaningful tags or annotations to raw data, typically in the form of text, images, audio, or video. These labels provide context and meaning to the data, enabling machine learning algorithms to learn and make predictions. What is Data Labeling for Machine Learning?

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LinkedIn Engineering

MARCH 23, 2023

Co-Authors: Yuhong Cheng , Shangjin Zhang , Xinyu Liu, and Yi Pan Efficient data processing is crucial in reducing learning curves, simplifying maintenance efforts, and decreasing operational complexity. By unifying these pipelines, we have saved 94% of processing time. Samza , Spark and Apache Flink ).

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Christophe Blefari

MARCH 22, 2024

Friday routine ( credits ) It's Friday and it's Data News. I don't go into too much detail about the magic of Data News, but every Friday is the same. Usually all the process takes me a whole Friday. The process works well, but as you can see, because I use fresh news, it's just-in-time. on April 10.

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Databand.ai

JULY 19, 2023

Complete Guide to Data Ingestion: Types, Process, and Best Practices Helen Soloveichik July 19, 2023 What Is Data Ingestion? Data Ingestion is the process of obtaining, importing, and processing data for later use or storage in a database. In this article: Why Is Data Ingestion Important?

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phData: Data Engineering

SEPTEMBER 19, 2023

With the amount of data companies are using growing to unprecedented levels, organizations are grappling with the challenge of efficiently managing and deriving insights from these vast volumes of structured and unstructured data. What is a Data Lake?

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Striim

NOVEMBER 17, 2023

In today’s data-driven world, the ability to leverage real-time data for machine learning applications is a game-changer. Real-time data processing in the world of machine learning allows data scientists and engineers to focus on model development and monitoring.

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Engineering at Meta

MAY 22, 2024

In recent years, Meta’s data management systems have evolved into a composable architecture that creates interoperability, promotes reusability, and improves engineering efficiency. Data is at the core of every product and service at Meta. We needed to change our thinking to be able to move faster.

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Maxime Beauchemin

JANUARY 7, 2018

Batch data processing  — historically known as ETL —  is extremely challenging. In this post, we’ll explore how applying the functional programming paradigm to data engineering can bring a lot of clarity to the process. The greater the claim made using analytics, the greater the scrutiny on the process should be.

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AltexSoft

AUGUST 25, 2021

And this technology of Natural Language Processing is available to all businesses. Available methods for text processing and which one to choose. Specifics of data used in NLP. What is Natural Language Processing? Here are some big text processing types and how they can be applied in real life. Main NLP use cases.

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Databand.ai

JULY 6, 2023

Data Integrity Testing: Goals, Process, and Best Practices Niv Sluzki July 6, 2023 What Is Data Integrity Testing? Data integrity testing refers to the process of validating the accuracy, consistency, and reliability of data stored in databases, data warehouses, or other data storage systems.

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DataKitchen

MAY 10, 2024

Harnessing Data Observability Across Five Key Use Cases The ability to monitor, validate, and ensure data accuracy across its lifecycle is not just a luxury—it’s a necessity. Data Evaluation Before new data sets are introduced into production environments, they must be thoroughly evaluated and cleaned.

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DareData

JUNE 11, 2024

With its capabilities of efficiently training deep learning models (with GPU-ready features), it has become a machine learning engineer and data scientist’s best friend when it comes to train complex neural network algorithms. We’ve seen how to use custom data in the context of the library. Let’s start! ok, hard to visualize!

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DataKitchen

DECEMBER 9, 2022

ChatGPT> DataOps, or data operations, is a set of practices and technologies that organizations use to improve the speed, quality, and reliability of their data analytics processes. The goal of DataOps is to help organizations make better use of their data to drive business decisions and improve outcomes.

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Machine Learning Data Preparation Government Data Analytics 52

DareData

FEBRUARY 28, 2024

In this post of the PyTorch Introduction, we’ll learn how to use custom datasets with PyTorch, particularly tabular, vision and text data PyTorch is one of the hottest libraries in the Deep Learning field right now. We’ve used few custom datasets in our examples and previous blog posts. Let’s start!

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DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Mastering Data Production (#3) Introduction Managing the production phase of data analytics is a daunting challenge. Overseeing multi-tool, multi-dataset, and multi-hop data processes ensures high-quality outputs.

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Knowledge Hut

JUNE 10, 2024

As a seasoned data scientist, I understand the pivotal role data plays in our field. Handling vast amounts of data is essential for maximizing career opportunities. The exponential growth of data, with 55-65 percent being unstructured, as reported by Forbes.com, poses significant challenges for analysis.

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Team Data Science

NOVEMBER 28, 2020

As a data scientist, I always felt a missing link between my developed models and putting them in the production process. This thought led me to my data engineering adventure. I am confident that learning data engineering will make me a better data scientist. Where did I start? Stay tuned!

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Tweag

APRIL 19, 2023

To allow innovation in medical imaging with AI, we need efficient and affordable ways to store and process these WSIs at scale. OpenSlide test data: CMU-1.tiff Marini et al This results in a very large amount of data for a single slide, often a few gigabytes per slide, which is all stored in one big file.

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Team Data Science

MARCH 15, 2020

I am taking you through my recent experience to find a dataset for my project. Industry Search To work with data, I need to narrow down the industry like health care, finance, insurance or other. I defined a few sources in my earlier blog post, which will give a sneak peek of techniques to extract industries.

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Knowledge Hut

APRIL 23, 2024

In the modern data-driven landscape, organizations continuously explore avenues to derive meaningful insights from the immense volume of information available. Two popular approaches that have emerged in recent years are data warehouse and big data. Data warehousing offers several advantages.

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ProjectPro

JANUARY 25, 2022

Did you know that, according to Linkedin, over 24,000 Big Data jobs in the US list Apache Spark as a required skill? Learning Spark has become more of a necessity to enter the Big Data industry. Python is one of the most extensively used programming languages for Data Analysis, Machine Learning , and data science tasks.

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Workfall

JULY 18, 2023

Behind the scenes, a team of data wizards tirelessly crunches mountains of data to make those recommendations sparkle. As one of those wizards, we’ve seen the challenges we face: the struggle to transform massive datasets into meaningful insights, all while keeping queries fast and our system scalable.

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Knowledge Hut

APRIL 25, 2024

Big data in information technology is used to improve operations, provide better customer service, develop customized marketing campaigns, and take other actions to increase revenue and profits. It is especially true in the world of big data. It is especially true in the world of big data. What Are Big Data T echnologies?

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Cloudyard

JUNE 8, 2024

Read Time: 1 Minute, 48 Second In this blog post, we’ll explore a sophisticated use case where we leverage Snowflake’s capabilities and the Snowpark Python API to process and analyze customer data. Imagine you’re a data analyst task with analyzing customer data for a large company.

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Data Engineering Weekly

DECEMBER 10, 2023

RudderStack, one of the leading alternatives to Segment , is the Warehouse Native CDP, built to help data teams deliver value across the entire data activation lifecycle, from collection to unification and activation. Lackluster AI/ML results often stem from poor data quality. Visit rudderstack.com to learn more.

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Databand.ai

AUGUST 30, 2023

Data testing tools: Key capabilities you should know Helen Soloveichik August 30, 2023 Data testing tools are software applications designed to assist data engineers and other professionals in validating, analyzing and maintaining data quality. There are several types of data testing tools.

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Christophe Blefari

JUNE 9, 2023

Rethinking the newsletter ( credits ) Here's a new edition of the Data News newsletter. I do things because I enjoy the process of doing things, not for the results. I can tease you that I'll create content about "Create a data platform in 2023", with live examples. Mikkel also showcases his tool synq.io

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Christophe Blefari

DECEMBER 31, 2023

Before moving on to the Data News, a bit of personal news, in December, I took part in the MotherDuck meetup in Berlin. End of January, on the 31st I'll speak at a Modern Data Stack conf in Paris, still about DuckDB, but this time in French. Enjoy this last 2023 Data News. How to know if your data team is successful?

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Pinterest Engineering

JULY 25, 2023

Soam Acharya | Data Engineering Oversight; Keith Regier | Data Privacy Engineering Manager Background Businesses collect many different types of data. Each dataset needs to be securely stored with minimal access granted to ensure they are used appropriately and can easily be located and disposed of when necessary.

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Databand.ai

AUGUST 30, 2023

8 Data Quality Monitoring Techniques & Metrics to Watch Niv Sluzki August 30, 2023 What Is Data Quality Monitoring? Data quality monitoring refers to the assessment, measurement, and management of an organization’s data in terms of accuracy, consistency, and reliability.

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DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Effective Data Anomaly Monitoring (#2) Introduction Ensuring the accuracy and timeliness of data ingestion is a cornerstone for maintaining the integrity of data systems. This process is critical as it ensures data quality from the onset.

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RandomTrees

FEBRUARY 2, 2024

GPT-based data engineering accelerators make the working of data more accessible. These accelerators use GPT models to do data tasks faster, fix any issues, and save a lot of time. GPT models change data in simple language and also provide summaries and explanations. It is the first AI-powered conversational data analyst.

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DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Fast, Safe Development and Deployment (#4) Introduction The integrity and functionality of new code, tools, and configurations during the development and deployment stages are crucial. This process is critical as it ensures data quality from the onset. Will I Create A Failed Deploy?

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