Pickl AI

SEPTEMBER 17, 2024

Summary: This blog delves into the multifaceted world of Big Data, covering its defining characteristics beyond the 5 V’s, essential technologies and tools for management, real-world applications across industries, challenges organisations face, and future trends shaping the landscape.

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IBM Journey to AI blog

SEPTEMBER 5, 2024

Everything is data—digital messages, emails, customer information, contracts, presentations, sensor data—virtually anything humans interact with can be converted into data, analyzed for insights or transformed into a product. Managing this level of oversight requires adept handling of large volumes of data.

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Dataconomy

SEPTEMBER 4, 2023

Text analytics is crucial for sentiment analysis, content categorization, and identifying emerging trends. Big data analytics: Big data analytics is designed to handle massive volumes of data from various sources, including structured and unstructured data.

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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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Precisely

AUGUST 8, 2023

Data governance is rapidly shifting from a leading-edge practice to a must-have framework for today’s enterprises. Although the term has been around for several decades, it is only now emerging as a widespread practice, as organizations experience the pain and compliance challenges associated with ungoverned data.

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Data Science Dojo

JANUARY 12, 2023

Additionally, unprocessed, raw data is pliable and suitable for machine learning. This implies that data that may never be needed is not wasting storage space. Data lake vs data warehouse: Which is right for me? It may be easily evaluated for any purpose. Businesses frequently require both.

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Pickl AI

DECEMBER 2, 2024

Summary: Big Data encompasses vast amounts of structured and unstructured data from various sources. Key components include data storage solutions, processing frameworks, analytics tools, and governance practices. Key Takeaways Big Data originates from diverse sources, including IoT and social media.

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Pickl AI

NOVEMBER 25, 2024

Summary: Big Data encompasses vast amounts of structured and unstructured data from various sources. Key components include data storage solutions, processing frameworks, analytics tools, and governance practices. Key Takeaways Big Data originates from diverse sources, including IoT and social media.

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Pickl AI

SEPTEMBER 11, 2024

Summary: Big Data as a Service (BDaaS) offers organisations scalable, cost-effective solutions for managing and analysing vast data volumes. By outsourcing Big Data functionalities, businesses can focus on deriving insights, improving decision-making, and driving innovation while overcoming infrastructure complexities.

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AWS Machine Learning Blog

AUGUST 21, 2024

Conclusion In this post, we covered an end-to-end integration of SageMaker Canvas and Amazon DataZone, including infrastructure controls, sharing and consuming data assets, and creating and publishing ML models. This integration provides a powerful solution for data governance, collaboration, and reusability across ML projects.

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Pickl AI

MARCH 19, 2023

Dealing with a large volume of structured and unstructured data requires meticulous work and precision. Data scientists and Big Data analytics work rigorously to derive useful insights. Big Data has many benefits, as it improves decision-making, develops new products and reduce costs.

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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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The Data Administration Newsletter

APRIL 6, 2021

There is an ever-increasing awareness of concerns about data privacy, corporate data breaches, increasing demands for regulatory compliance. There are also emerging concerns about the ways that big data analytics potentially influence and bias automated decision-making.

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Precisely

DECEMBER 20, 2022

Read more > #4 4 Real-World Examples of Financial Institutions Making Use of Big Data Big data has moved beyond “new tech” status and into mainstream use. Within the financial industry, there are some specialized uses for data integration and big data analytics.

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Pickl AI

JULY 29, 2024

It utilises the Hadoop Distributed File System (HDFS) and MapReduce for efficient data management, enabling organisations to perform big data analytics and gain valuable insights from their data. In a Hadoop cluster, data stored in the Hadoop Distributed File System (HDFS), which spreads the data across the nodes.

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Heartbeat

OCTOBER 23, 2023

Image from "Big Data Analytics Methods" by Peter Ghavami Here are some critical contributions of data scientists and machine learning engineers in health informatics: Data Analysis and Visualization: Data scientists and machine learning engineers are skilled in analyzing large, complex healthcare datasets.

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AWS Machine Learning Blog

FEBRUARY 13, 2024

The following diagram shows two different data scientist teams, from two different AWS accounts, who share and use the same central feature store to select the best features needed to build their ML models. This enhances data accessibility and utilization, allowing teams in different accounts to use shared features for their ML workflows.

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Pickl AI

AUGUST 24, 2023

This minimizes the risk of data loss and downtime. Innovation: Cloud Computing encourages innovation by providing access to advanced technologies and services, such as artificial intelligence, machine learning, big data analytics, and more.

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AWS Machine Learning Blog

AUGUST 21, 2023

Our customers wanted the ability to connect to Amazon EMR to run ad hoc SQL queries on Hive or Presto to query data in the internal metastore or external metastore (such as the AWS Glue Data Catalog ), and prepare data within a few clicks. Isha Dua is a Senior Solutions Architect based in the San Francisco Bay Area.

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How to Learn Machine Learning

APRIL 23, 2023

Also included in the need for flexibility are data governance, data integration, and data exploration, all of which require crucial supervision and monitoring. These processes ensure all data are evaluated and all scopes are checked, tested, and assessed.

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Alation

FEBRUARY 13, 2020

Alation’s TrustCheck technology enables a new and modern approach to agile data governance. TrustCheck is helping customers improve analytics behavior and ensure compliance, without restricting analytical agility. And, it recently received the 2018 Digital Innovation Award for Big Data from Ventana Research.

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Precisely

OCTOBER 16, 2023

Rapid advancements in digital technologies are transforming cloud-based computing and cloud analytics. Big data analytics, IoT, AI, and machine learning are revolutionizing the way businesses create value and competitive advantage. In a connected mainframe/cloud environment, data is often diverse and fragmented.

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AWS Machine Learning Blog

NOVEMBER 12, 2024

Their data pipeline (as shown in the following architecture diagram) consists of ingestion, storage, ETL (extract, transform, and load), and a data governance layer. Multi-source data is initially received and stored in an Amazon Simple Storage Service (Amazon S3) data lake.

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