Smart Data Collective

JULY 20, 2021

Big data technology is incredibly important in modern business. One of the most important applications of big data is with building relationships with customers. These software tools rely on sophisticated big data algorithms and allow companies to boost their sales, business productivity and customer retention.

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Data Science Blog

JUNE 27, 2023

Big Data Analytics stands apart from conventional data processing in its fundamental nature. In the realm of Big Data, there are two prominent architectural concepts that perplex companies embarking on the construction or restructuring of their Big Data platform: Lambda architecture or Kappa architecture.

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Big Data Big Data Apache Kafka Database 130

IBM Data Science in Practice

APRIL 7, 2025

Graceful External Termination: Handling Pod Deletions in Kubernetes Data Ingestion and Streaming Jobs When running big-data pipelines in Kubernetes, especially streaming jobs, its easy to overlook how these jobs deal with termination. If not handled correctly, this can lead to locks, data issues, and a negative user experience.

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Python ETL Data Pipeline Big Data 130

Smart Data Collective

JULY 9, 2023

ETL (Extract, Transform, Load) is a crucial process in the world of data analytics and business intelligence. In this article, we will explore the significance of ETL and how it plays a vital role in enabling effective decision making within businesses. What is ETL? Let’s break down each step: 1.

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ETL Data Quality Data Warehouse Analytics 105

insideBIGDATA

JULY 1, 2024

In this contributed article, Adrian Kunzle, Chief Technology Officer at Own Company, discusses strategies around using historical data to understand their businesses better and fill gaps are often overlooked.

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Data Warehouse ETL AI AI 221

Smart Data Collective

MAY 31, 2022

Big data technology is having a huge impact on the state of modern business. The technology surrounding big data has evolved significantly in recent years, which means that smart businesses will have to take steps to keep up with it. What is Data Activation? It Started Reverse ETL.

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ETL Data Silos Data Warehouse Big Data 142

ODSC - Open Data Science

JUNE 19, 2023

With the advent of big data in the modern world, RTOS is becoming increasingly important. As software expert Tim Mangan explains, a purpose-built real-time OS is more suitable for apps that involve tons of data processing. The Big Data and RTOS connection IoT and embedded devices are among the biggest sources of big data.

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Big Data Big Data Artificial Intelligence Artificial Intelligence 96

Data Science Dojo

JULY 6, 2023

Data engineering tools are software applications or frameworks specifically designed to facilitate the process of managing, processing, and transforming large volumes of data. It integrates seamlessly with other AWS services and supports various data integration and transformation workflows.

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Dataconomy

FEBRUARY 23, 2023

Data engineers play a crucial role in managing and processing big data. They are responsible for designing, building, and maintaining the infrastructure and tools needed to manage and process large volumes of data effectively. They must also ensure that data privacy regulations, such as GDPR and CCPA , are followed.

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Big Data Big Data Data Engineering Data Engineer 91

Flipboard

NOVEMBER 7, 2023

“Data is at the center of every application, process, and business decision,” wrote Swami Sivasubramanian, VP of Database, Analytics, and Machine Learning at AWS, and I couldn’t agree more. A common pattern customers use today is to build data pipelines to move data from Amazon Aurora to Amazon Redshift.

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Data Science Dojo

OCTOBER 31, 2024

Key Skills Proficiency in SQL is essential, along with experience in data visualization tools such as Tableau or Power BI. Strong analytical skills and the ability to work with large datasets are critical, as is familiarity with data modeling and ETL processes.

Data Science 195

Data Science Data Scientist Machine Learning Machine Learning 195

Dataconomy

APRIL 18, 2022

Combining data from various sources into a single, coherent picture is known as data integration. The ingestion procedure starts the integration process, including cleaning, ETL mapping, and transformation. There is no one-size-fits-all solution when.

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ETL Business Intelligence Business Intelligence Analytics 186

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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Big Data Big Data Apache Kafka Data Pipeline 59

Precisely

JUNE 27, 2024

The efficiency of ETL integration can make or break the rest of your data management workflow. Want to get the most from your ETL processes? Keep reading for high-performance ETL best practices. 8 ETL best practices For optimum integration results, here’s eight of our best tips.

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ETL Data Silos Data Quality Database 52

Pickl AI

OCTOBER 6, 2024

Summary: This blog explores the key differences between ETL and ELT, detailing their processes, advantages, and disadvantages. Understanding these methods helps organizations optimize their data workflows for better decision-making. What is ETL? ETL stands for Extract, Transform, and Load.

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Women in Big Data

NOVEMBER 27, 2024

Optimized for analytical processing, it uses specialized data models to enhance query performance and is often integrated with business intelligence tools, allowing users to create reports and visualizations that inform organizational strategies. Pay close attention to the cost structure, including any potential hidden fees.

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Data Warehouse Big Data Big Data Azure 59

Precisely

JANUARY 9, 2025

The magic of the data warehouse was figuring out how to get data out of these transactional systems and reorganize it in a structured way optimized for analysis and reporting. Then came Big Data and Hadoop! The big data boom was born, and Hadoop was its poster child.

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Data Warehouse Hadoop Data Governance Data Lakes 119

Pickl AI

AUGUST 9, 2024

Summary: A comprehensive Big Data syllabus encompasses foundational concepts, essential technologies, data collection and storage methods, processing and analysis techniques, and visualisation strategies. Fundamentals of Big Data Understanding the fundamentals of Big Data is crucial for anyone entering this field.

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Big Data Big Data Big Data Analytics Big Data Analytics 52

Smart Data Collective

OCTOBER 20, 2021

The upsurge of data (with the introduction of non-traditional data sources like streaming data, machine logs, etc.) along with traditional ones challenge old models of data integration. Why is Data Integration a Challenge for Enterprises? Legacy solutions lack precision and speed while handling big data.

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ML ML Big Data Big Data 133

Smart Data Collective

OCTOBER 17, 2022

Big data is shaping our world in countless ways. Data powers everything we do. Exactly why, the systems have to ensure adequate, accurate and most importantly, consistent data flow between different systems. Its underlying Singer framework allows the data teams to customize the pipeline with ease.

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Data Pipeline Data Warehouse ETL Data Lakes 145

Data Science Blog

SEPTEMBER 19, 2023

In the contemporary age of Big Data, Data Warehouse Systems and Data Science Analytics Infrastructures have become an essential component for organizations to store, analyze, and make data-driven decisions. So why using IaC for Cloud Data Infrastructures?

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Data Warehouse Azure SQL Database 259

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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Database AWS SQL ETL 111

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.

Big Data 130

Big Data Big Data Data Engineering Data Engineer 130

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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Pickl AI

JANUARY 27, 2025

Summary: HDFS in Big Data uses distributed storage and replication to manage massive datasets efficiently. By co-locating data and computations, HDFS delivers high throughput, enabling advanced analytics and driving data-driven insights across various industries. It fosters reliability. between 2024 and 2030.

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Hadoop Big Data Big Data Clustering 52

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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ML ML Python ETL 128

Smart Data Collective

AUGUST 28, 2021

In comparison, data warehouses are only capable of storing structured data. Since data warehouses can deal only with structured data, they also require extract, transform, and load (ETL) processes to transform the raw data into a target structure ( Schema on Write ) before storing it in the warehouse.

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Data Lakes Data Warehouse ETL Data Scientist 140

Smart Data Collective

AUGUST 4, 2022

A growing number of businesses are relying on big data technology to improve productivity and address some of their most pressing challenges. Global companies are projected to spend over $297 billion on big data by 2030. Data technology has proven to be remarkably helpful for many businesses. Problem Statement.

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SQL Database Big Data Big Data 136

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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Data Science Big Data Big Data Deep Learning 105

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. I worked extensively with ETL processes, PostgreSQL, and later, enterprise-scale data systems.

ML 52

ML ML Big Data Big Data 52

Flipboard

NOVEMBER 24, 2023

The SnapLogic Intelligent Integration Platform (IIP) enables organizations to realize enterprise-wide automation by connecting their entire ecosystem of applications, databases, big data, machines and devices, APIs, and more with pre-built, intelligent connectors called Snaps.

Database 158

Database AWS ETL SQL 158

Flipboard

JUNE 26, 2023

Transform raw insurance data into CSV format acceptable to Neptune Bulk Loader , using an AWS Glue extract, transform, and load (ETL) job. When the data is in CSV format, use an Amazon SageMaker Jupyter notebook to run a PySpark script to load the raw data into Neptune and visualize it in a Jupyter notebook.

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AWS ML ML ETL 123

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? Big data analytics from 2022 show a dramatic surge in information consumption.

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Big Data Big Data Data Engineering Data Engineer 119

Dataconomy

SEPTEMBER 4, 2023

Big data analytics: Big data analytics is designed to handle massive volumes of data from various sources, including structured and unstructured data. Big data analytics is essential for organizations dealing with large-scale data, such as social media platforms, e-commerce giants, and scientific research.

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Analytics Analytics Big Data Analytics Big Data Analytics 203

Smart Data Collective

MAY 20, 2019

We’re well past the point of realization that big data and advanced analytics solutions are valuable — just about everyone knows this by now. Big data alone has become a modern staple of nearly every industry from retail to manufacturing, and for good reason.

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AWS Machine Learning Blog

FEBRUARY 2, 2024

The embeddings are captured in Amazon Simple Storage Service (Amazon S3) via Amazon Kinesis Data Firehose , and we run a combination of AWS Glue extract, transform, and load (ETL) jobs and Jupyter notebooks to perform the embedding analysis. Set the parameters for the ETL job as follows and run the job: Set --job_type to BASELINE.

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Flipboard

DECEMBER 11, 2024

About the Authors Noritaka Sekiyama is a Principal Big Data Architect on the AWS Glue team. Chiho Sugimoto is a Cloud Support Engineer on the AWS Big Data Support team. She is passionate about helping customers build data lakes using ETL workloads. Big Data Architect. Zach Mitchell is a Sr.

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SQL AWS Data Lakes AI 160