Pickl AI

DECEMBER 9, 2024

Summary: This article provides a comprehensive guide on Big Data interview questions, covering beginner to advanced topics. Introduction Big Data continues transforming industries, making it a vital asset in 2025. The global Big Data Analytics market, valued at $307.51 What is Big Data?

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Pickl AI

DECEMBER 26, 2024

Summary: “Data Science in a Cloud World” highlights how cloud computing transforms Data Science by providing scalable, cost-effective solutions for big data, Machine Learning, and real-time analytics. This accessibility democratises Data Science, making it available to businesses of all sizes.

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Flipboard

MARCH 3, 2023

Data proliferation has become a norm and as organizations become more data driven, automating data pipelines that enable data ingestion, curation, …

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

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Smart Data Collective

APRIL 2, 2019

These massive storage pools of data are among the most non-traditional methods of data storage around and they came about as companies raced to embrace the trend of Big Data Analytics which was sweeping the world in the early 2010s. Big Data is, well…big.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

Rajesh Nedunuri is a Senior Data Engineer within the Amazon Worldwide Returns and ReCommerce Data Services team. He specializes in designing, building, and optimizing large-scale data solutions.

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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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AWS Machine Learning Blog

FEBRUARY 13, 2024

Let’s demystify this using the following personas and a real-world analogy: Data and ML engineers (owners and producers) – They lay the groundwork by feeding data into the feature store Data scientists (consumers) – They extract and utilize this data to craft their models Data engineers serve as architects sketching the initial blueprint.

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IBM Journey to AI blog

AUGUST 6, 2024

Big data analytics Serverless dramatically reduces the cost and complexity of writing and deploying code for big data applications. Additionally, serverless’ always-on capabilities mean data pipelines can be designed in a way to react to real-time changes in data and change application logic accordingly.

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IBM Journey to AI blog

SEPTEMBER 4, 2024

Kafka helps simplify the communication between customers and businesses, using its data pipeline to accurately record events and keep records of orders and cancellations—alerting all relevant parties in real-time.

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Pickl AI

SEPTEMBER 5, 2024

Advanced Analytics: Tools like Azure Machine Learning and Azure Databricks provide robust capabilities for building, training, and deploying Machine Learning models. Unified Data Services: Azure Synapse Analytics combines big data and data warehousing, offering a unified analytics experience.

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The MLOps Blog

AUGUST 11, 2023

Internally within Netflix’s engineering team, Meson was built to manage, orchestrate, schedule, and execute workflows within ML/Data pipelines. Meson managed the lifecycle of ML pipelines, providing functionality such as recommendations and content analysis, and leveraged the Single Leader Architecture.

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Towards AI

NOVEMBER 15, 2024

Image by author Hello Welcome to the Azure Data Engineer Project Series, Before building the Data Architecture or any data pipelines in any cloud platform, we need to know the basic terms each platform uses and how the platform will work. Here is the data pipeline building from ADLS to Azure SQL DB.

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AWS Machine Learning Blog

NOVEMBER 12, 2024

The first step in developing and deploying generative AI use cases is having a well-defined data strategy. Their data pipeline (as shown in the following architecture diagram) consists of ingestion, storage, ETL (extract, transform, and load), and a data governance layer. Agmatix’s technology architecture is built on AWS.

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Pickl AI

MARCH 13, 2025

RabbitMQ ensures reliable, structured message delivery, while Kafka excels in real-time, high-volume data streaming. Choosing between them depends on your systems needsRabbitMQ is best for workflows, while Kafka is ideal for event-driven architectures and big data processing. Thats where message brokers come in.

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