ODSC - Open Data Science

SEPTEMBER 27, 2023

In the ever-evolving world of big data, managing vast amounts of information efficiently has become a critical challenge for businesses across the globe. Understanding Data Lakes A data lake is a centralized repository that stores structured, semi-structured, and unstructured data in its raw format.

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Smart Data Collective

AUGUST 28, 2021

Data lakes and data warehouses are probably the two most widely used structures for storing data. In this article, we will explore both, unfold their key differences and discuss their usage in the context of an organization. Data Warehouses and Data Lakes in a Nutshell. Key Differences.

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Precisely

MARCH 9, 2023

Data warehouse vs. data lake, each has their own unique advantages and disadvantages; it’s helpful to understand their similarities and differences. In this article, we’ll focus on a data lake vs. data warehouse. It is often used as a foundation for enterprise data lakes.

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Smart Data Collective

MARCH 30, 2022

Azure Data Lake Storage Gen2 is based on Azure Blob storage and offers a suite of big data analytics features. If you don’t understand the concept, you might want to check out our previous article on the difference between data lakes and data warehouses. Determine your preparedness.

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insideBIGDATA

APRIL 22, 2024

In this contributed article, Sida Shen, product marketing manager, CelerData, discusses how data lakehouse architectures promise the combined strengths of data lakes and data warehouses, but one question arises: why do we still find the need to transfer data from these lakehouses to proprietary data warehouses?

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The Data Administration Newsletter

JANUARY 3, 2023

For a while now, vendors have been advocating that people put their data in a data lake when they put their data in the cloud. The Data Lake The idea is that you put your data into a data lake. Then, at a later point in time, the end user analyst can come along and […].

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The Data Administration Newsletter

JUNE 5, 2024

In the ever-evolving landscape of data management, two key concepts have emerged as essential components for organizations seeking to harness the power of their data: data marts and data lakes. Understanding the distinctions […]

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ODSC - Open Data Science

SEPTEMBER 29, 2023

But, the amount of data companies must manage is growing at a staggering rate. Research analyst firm Statista forecasts global data creation will hit 180 zettabytes by 2025. One way to address this is to implement a data lake: a large and complex database of diverse datasets all stored in their original format.

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insideBIGDATA

AUGUST 28, 2024

Dremio, the unified lakehouse platform for self-service analytics and AI, announced a breakthrough in data lake analytics performance capabilities, extending its leadership in self-optimizing, autonomous Iceberg data management.

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ODSC - Open Data Science

JUNE 7, 2023

To make your data management processes easier, here’s a primer on data lakes, and our picks for a few data lake vendors worth considering. What is a data lake? First, a data lake is a centralized repository that allows users or an organization to store and analyze large volumes of data.

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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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Dataversity

JANUARY 28, 2021

It has been ten years since Pentaho Chief Technology Officer James Dixon coined the term “data lake.” While data warehouse (DWH) systems have had longer existence and recognition, the data industry has embraced the more […]. The post A Bridge Between Data Lakes and Data Warehouses appeared first on DATAVERSITY.

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Dataversity

AUGUST 27, 2021

If this time 10 years ago you were working in data and analytics, something was about to happen that would go on to dominate a large part of your professional life. I’m talking about the emergence of “big data.” The post Big Data at 10: Did Bigger Mean Better? appeared first on DATAVERSITY.

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Smart Data Collective

AUGUST 9, 2019

Big data in the gaming industry has played a phenomenal role in the field. We have previously talked about the benefits of using big data by gaming providers that offer cash games, such as slots. However, more mainstream games use big data as well. Big Data is the Lynchpin of the Fortnite Gaming Experience.

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ODSC - Open Data Science

MAY 7, 2024

In this episode, James Serra, author of “Deciphering Data Architectures: Choosing Between a Modern Data Warehouse, Data Fabric, Data Lakehouse, and Data Mesh” joins us to discuss his book and dive into the current state and possible future of data architectures. Finally, like what you hear?

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insideBIGDATA

FEBRUARY 2, 2024

In this contributed article, Tom Scott, CEO of Streambased, outlines the path event streaming systems have taken to arrive at the point where they must adopt analytical use cases and looks at some possible futures in this area.

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The Data Administration Newsletter

MARCH 15, 2022

In her groundbreaking article, How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh, Zhamak Dehghani made the case for building data mesh as the next generation of enterprise data platform architecture.

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Smart Data Collective

FEBRUARY 23, 2022

Traditional relational databases provide certain benefits, but they are not suitable to handle big and various data. That is when data lake products started gaining popularity, and since then, more companies introduced lake solutions as part of their data infrastructure. AWS Athena and S3.

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AWS Machine Learning Blog

OCTOBER 20, 2023

Data and governance foundations – This function uses a data mesh architecture for setting up and operating the data lake, central feature store, and data governance foundations to enable fine-grained data access. This framework considers multiple personas and services to govern the ML lifecycle at scale.

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ODSC - Open Data Science

MARCH 23, 2023

Real-time Analytics & Built-in Machine Learning Models with a Single Database Akmal Chaudhri, Senior Technical Evangelist at SingleStore, explores the importance of delivering real-time experiences in today’s big data industry and how data models and algorithms rely on powerful and versatile data infrastructure.

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Smart Data Collective

DECEMBER 13, 2022

However, computerization in the digital age creates massive volumes of data, which has resulted in the formation of several industries, all of which rely on data and its ever-increasing relevance. Data analytics and visualization help with many such use cases. It is the time of big data.

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DagsHub

NOVEMBER 11, 2024

Social media conversations, comments, customer reviews, and image data are unstructured in nature and hold valuable insights, many of which are still being uncovered through advanced techniques like Natural Language Processing (NLP) and machine learning. Many find themselves swamped by the volume and complexity of unstructured data.

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Pickl AI

NOVEMBER 4, 2024

As organisations increasingly rely on data to drive decision-making, understanding the fundamentals of Data Engineering becomes essential. The global Big Data and Data Engineering Services market, valued at USD 51,761.6 They are crucial in ensuring data is readily available for analysis and reporting.

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AWS Machine Learning Blog

OCTOBER 24, 2024

RAG complements LLMs with an external knowledge base that’s typically built using a vector database hydrated with vector-encoded knowledge articles. It’s an article with specialized content that LLMs cannot answer without using RAG. About the Authors Noritaka Sekiyama is a Principal Big Data Architect on the AWS Glue team.

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ODSC - Open Data Science

FEBRUARY 2, 2023

Big Data As datasets become larger and more complex, knowing how to work with them will be key. Big data isn’t an abstract concept anymore, as so much data comes from social media, healthcare data, and customer records, so knowing how to parse all of that is needed.

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Smart Data Collective

AUGUST 13, 2019

One report showed that Caesars is investing $1 billion in big data. I still remember playing my favorite games growing up, before machine learning was a thing or big data was a household word. I have read a lot of articles on the applicability of machine learning in digital gaming. Slots is a prime example.

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Smart Data Collective

FEBRUARY 20, 2022

Whether it’s data management, analytics, or scalability, AWS can be the top-notch solution for any SaaS company. In this article we will list 10 things AWS can do for your SaaS company. This article finally gets to the core question we started with: what can AWS do for your SaaS business? Data storage databases.

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Pickl AI

OCTOBER 10, 2024

Data transformation tools simplify this process by automating data manipulation, making it more efficient and reducing errors. These tools enable seamless data integration across multiple sources, streamlining data workflows. What is Data Transformation?

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ODSC - Open Data Science

JULY 14, 2023

Originally posted on OpenDataScience.com Read more data science articles on OpenDataScience.com , including tutorials and guides from beginner to advanced levels! The session participants will learn the theory behind compound sparsity, state-of-the-art techniques, and how to apply it in practice using the Neural Magic platform.

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Dataversity

JANUARY 6, 2021

We live in an era of big data. Amazingly, statistics show that around 90 percent of this data is only two years old. However, Data Management and structuring are notoriously complex. […]. The post The Need for Flexible Data Management: Why Is Data Flexibility So Important?

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Dataversity

FEBRUARY 24, 2021

The amount of data generated in the digital world is increasing by the minute! This massive amount of data is termed “big data.” We may classify the data as structured, unstructured, or semi-structured. Data that is structured or semi-structured is relatively easy to store, process, and analyze. […].

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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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DagsHub

OCTOBER 23, 2024

Managing unstructured data is essential for the success of machine learning (ML) projects. Without structure, data is difficult to analyze and extracting meaningful insights and patterns is challenging. This article will discuss managing unstructured data for AI and ML projects. How to properly manage unstructured data.

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Alation

MARCH 21, 2023

To fully realize data’s value, organizations in the travel industry need to dismantle data silos so that they can securely and efficiently leverage analytics across their organizations. What is big data in the travel and tourism industry? Using Alation, ARC automated the data curation and cataloging process. “So

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ODSC - Open Data Science

MARCH 11, 2024

Data Morph: A Cautionary Tale of Summary Statistics Visualization in Bayesian Workflow Using Python or R Harnessing Bayesian Statistics for Business-Centric Data Science Data Engineering and Big Data Join this track to learn the latest techniques and processes to analyze raw data and automate data into mechanical processes and algorithms.

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ODSC - Open Data Science

OCTOBER 20, 2023

HPCC Systems — The Kit and Kaboodle for Big Data and Data Science Bob Foreman | Software Engineering Lead | LexisNexis/HPCC Join this session to learn how ECL can help you create powerful data queries through a comprehensive and dedicated data lake platform.

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ODSC - Open Data Science

DECEMBER 19, 2023

Data Engineer Data engineers are responsible for the end-to-end process of collecting, storing, and processing data. They use their knowledge of data warehousing, data lakes, and big data technologies to build and maintain data pipelines. So what are you waiting for? Get your pass today!

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