Women in Big Data

OCTOBER 9, 2024

With the explosive growth of big data over the past decade and the daily surge in data volumes, it’s essential to have a resilient system to manage the vast influx of information without failures. The success of any data initiative hinges on the robustness and flexibility of its big data pipeline.

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Data Science Blog

JUNE 21, 2023

Process Mining demands Big Data in 99% of the cases, releasing bad developed extraction jobs will end in big cost chunks down the value stream. Process Mining – Data Extraction The data extraction for process mining should be well planed and match the data strategy of the organization.

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Hacker News

JULY 18, 2024

ABOUT EVENTUAL Eventual is a data platform that helps data scientists and engineers build data applications across ETL, analytics and ML/AI. OUR PRODUCT IS OPEN-SOURCE AND USED AT ENTERPRISE SCALE Our distributed data engine Daft [link] is open-sourced and runs on 800k CPU cores daily.

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AWS Machine Learning Blog

NOVEMBER 20, 2024

She has experience across analytics, big data, ETL, cloud operations, and cloud infrastructure management. Data Engineer at Amazon Ads. He builds and manages data-driven solutions for recommendation systems, working together with a diverse and talented team of scientists, engineers, and product managers.

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Pickl AI

APRIL 6, 2023

Accordingly, one of the most demanding roles is that of Azure Data Engineer Jobs that you might be interested in. The following blog will help you know about the Azure Data Engineering Job Description, salary, and certification course. How to Become an Azure Data Engineer?

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Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

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Smart Data Collective

OCTOBER 17, 2022

While growing data enables companies to set baselines, benchmarks, and targets to keep moving ahead, it poses a question as to what actually causes it and what it means to your organization’s engineering team efficiency. What’s causing the data explosion? Explosive data growth can be too much to handle.

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Pickl AI

JULY 25, 2023

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

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Mlearning.ai

MAY 16, 2023

Data engineering is a rapidly growing field that designs and develops systems that process and manage large amounts of data. There are various architectural design patterns in data engineering that are used to solve different data-related problems.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

JUNE 7, 2024

Summary: Choosing the right ETL tool is crucial for seamless data integration. Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high data quality, and informed decision-making capabilities. Choosing the right ETL tool is crucial for smooth data management.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

Previously, he was a Data & Machine Learning Engineer at AWS, where he worked closely with customers to develop enterprise-scale data infrastructure, including data lakes, analytics dashboards, and ETL pipelines. He specializes in designing, building, and optimizing large-scale data solutions.

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Becoming Human

JANUARY 23, 2023

Nowadays most businesses use data science, whether a business is product-based or service-based they use data science for their growth. Data Science and Big Data There is an Umbrella of Big data and what is Big Data?

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Flipboard

DECEMBER 11, 2024

Organizations are building data-driven applications to guide business decisions, improve agility, and drive innovation. Many of these applications are complex to build because they require collaboration across teams and the integration of data, tools, and services. Big Data Architect. He works based in Tokyo, Japan.

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Analytics Vidhya

FEBRUARY 27, 2023

Introduction Enterprises here and now catalyze vast quantities of data, which can be a high-end source of business intelligence and insight when used appropriately. Delta Lake allows businesses to access and break new data down in real time.

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Data Science Dojo

JULY 3, 2024

Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud. Data Processing and Analysis : Techniques for data cleaning, manipulation, and analysis using libraries such as Pandas and Numpy in Python.

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ODSC - Open Data Science

APRIL 4, 2024

Data Analytics in the Age of AI, When to Use RAG, Examples of Data Visualization with D3 and Vega, and ODSC East Selling Out Soon Data Analytics in the Age of AI Let’s explore the multifaceted ways in which AI is revolutionizing data analytics, making it more accessible, efficient, and insightful than ever before.

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Data Visualization Analytics Analytics Big Data Analytics 52

ODSC - Open Data Science

APRIL 3, 2023

Data Wrangling: Data Quality, ETL, Databases, Big Data The modern data analyst is expected to be able to source and retrieve their own data for analysis. Competence in data quality, databases, and ETL (Extract, Transform, Load) are essential.

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Analytics Analytics Data Analyst Data Science 52

Alation

JANUARY 17, 2023

A typical modern data stack consists of the following: A data warehouse. Data ingestion/integration services. Reverse ETL tools. Data orchestration tools. These tools are used to manage big data, which is defined as data that is too large or complex to be processed by traditional means.

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Pickl AI

OCTOBER 10, 2024

Enhanced Data Quality : These tools ensure data consistency and accuracy, eliminating errors often occurring during manual transformation. Scalability : Whether handling small datasets or processing big data, transformation tools can easily scale to accommodate growing data volumes.

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IBM Journey to AI blog

JANUARY 5, 2023

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

For instance, a notebook that monitors for model data drift should have a pre-step that allows extract, transform, and load (ETL) and processing of new data and a post-step of model refresh and training in case a significant drift is noticed. About the authors Anchit Gupta is a Senior Product Manager for Amazon SageMaker Studio.

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ODSC - Open Data Science

OCTOBER 18, 2024

The advent of big data, affordable computing power, and advanced machine learning algorithms has fueled explosive growth in data science across industries. However, research shows that up to 85% of data science projects fail to move beyond proofs of concept to full-scale deployment.

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Data Science Blog

FEBRUARY 4, 2023

In the era of Industry 4.0 , linking data from MES (Manufacturing Execution System) with that from ERP, CRM and PLM systems plays an important role in creating integrated monitoring and control of business processes.

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Cloud Data Data Science Business Intelligence Business Intelligence 147

ODSC - Open Data Science

OCTOBER 9, 2024

Efficient Incremental Processing with Apache Iceberg and Netflix Maestro Dimensional Data Modeling in the Modern Era Building Big Data Workflows: NiFi, Hive, Trino, & Zeppelin An Introduction to Data Contracts From Data Mess to Data Mesh — Data Management in the Age of Big Data and Gen AI Introduction to Containers for Data Science / Data Engineering (..)

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Mlearning.ai

FEBRUARY 9, 2023

I started working in Data Science right after graduating with an MS degree in Electrical and Computer Engineering from the University of California, Los Angeles (UCLA). If a random forest is not giving a big enough win over logistic regression, then the latter might be preferred for its lower complexity.

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DagsHub

OCTOBER 23, 2024

General Purpose Tools These tools help manage the unstructured data pipeline to varying degrees, with some encompassing data collection, storage, processing, analysis, and visualization. DagsHub's Data Engine DagsHub's Data Engine is a centralized platform for teams to manage and use their datasets effectively.

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Machine Learning Machine Learning Data Lakes AI 52

Flipboard

NOVEMBER 22, 2024

During development, data engineers often spend hours sifting through log files, analyzing execution plans, and making configuration changes to resolve issues. Troubleshooting these production issues requires extensive analysis of logs and metrics, often leading to extended downtimes and delayed insights from critical data pipelines.

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Pickl AI

JULY 18, 2024

In contrast, MongoDB uses a more straightforward query language that works well with JSON data structures. MongoDB’s horizontal scaling capabilities surpass relational databases’ typical vertical scaling limitations, making it suitable for big data applications. Data Engineering Interview Questions and Answers.

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Women in Big Data

MARCH 5, 2025

I had the pleasure of interviewing Anu Jekal , the CEO of Data Surge , a leading company in data and AI/ML. At Women in Big Data (WiBD), Anu has been a mentor and volunteer for almost 2 years. My career started as an operations engineer, where I quickly learned Linux the hard way. Q: Tell me more about Data Surge?

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ML ML Big Data Big Data 52

Flipboard

DECEMBER 4, 2024

However, building data-driven applications can be challenging. It often requires multiple teams working together and integrating various data sources, tools, and services. For example, creating a targeted marketing app involves data engineers, data scientists, and business analysts using different systems and tools.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

An example direct acyclic graph (DAG) might automate data ingestion, processing, model training, and deployment tasks, ensuring that each step is run in the correct order and at the right time. Though it’s worth mentioning that Airflow isn’t used at runtime as is usual for extract, transform, and load (ETL) tasks.

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