Alation

AUGUST 4, 2022

It’s been one decade since the “ Big Data Era ” began (and to much acclaim!). Analysts asked, What if we could manage massive volumes and varieties of data? Yet the question remains: How much value have organizations derived from big data? Big Data as an Enabler of Digital Transformation.

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Pickl AI

FEBRUARY 23, 2023

Organisations must store data in a safe and secure place for which Databases and Data warehouses are essential. You must be familiar with the terms, but Database and Data Warehouse have some significant differences while being equally crucial for businesses. What is Data Warehouse?

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IBM Journey to AI blog

APRIL 24, 2023

Data is reported from one central repository, enabling management to draw more meaningful business insights and make faster, better decisions. By running reports on historical data, a data warehouse can clarify what systems and processes are working and what methods need improvement.

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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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Pickl AI

JUNE 11, 2024

Summary: A data warehouse is a central information hub that stores and organizes vast amounts of data from different sources within an organization. Unlike operational databases focused on daily tasks, data warehouses are designed for analysis, enabling historical trend exploration and informed decision-making.

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Smart Data Collective

OCTOBER 13, 2021

There are countless examples of big data transforming many different industries. There is no disputing the fact that the collection and analysis of massive amounts of unstructured data has been a huge breakthrough. This is something that you can learn more about in just about any technology blog.

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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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Pickl AI

NOVEMBER 15, 2023

Discover the nuanced dissimilarities between Data Lakes and Data Warehouses. Data management in the digital age has become a crucial aspect of businesses, and two prominent concepts in this realm are Data Lakes and Data Warehouses. It acts as a repository for storing all the data.

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IBM Journey to AI blog

JUNE 15, 2023

It is comprised of commodity cloud object storage, open data and open table formats, and high-performance open-source query engines. To help organizations scale AI workloads, we recently announced IBM watsonx.data , a data store built on an open data lakehouse architecture and part of the watsonx AI and data platform.

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Pickl AI

SEPTEMBER 18, 2024

Summary: Netflix’s sophisticated Big Data infrastructure powers its content recommendation engine, personalization, and data-driven decision-making. As a pioneer in the streaming industry, Netflix utilises advanced data analytics to enhance user experience, optimise operations, and drive strategic decisions.

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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. option("multiLine", "true").option("header",

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Smart Data Collective

SEPTEMBER 8, 2021

The ETL process is defined as the movement of data from its source to destination storage (typically a Data Warehouse) for future use in reports and analyzes. The data is initially extracted from a vast array of sources before transforming and converting it to a specific format based on business requirements. Conclusion.

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IBM Journey to AI blog

APRIL 25, 2023

In a prior blog , we pointed out that warehouses, known for high-performance data processing for business intelligence, can quickly become expensive for new data and evolving workloads. To do so, Presto and Spark need to readily work with existing and modern data warehouse infrastructures.

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Pickl AI

MARCH 19, 2025

Data engineering is all about collecting, organising, and moving data so businesses can make better decisions. Handling massive amounts of data would be a nightmare without the right tools. In this blog, well explore the best data engineering tools that make data work easier, faster, and more reliable.

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IBM Journey to AI blog

OCTOBER 4, 2023

In fact, according in an IDC DataSphere study, IDC estimated that 10,628 exabytes (EB) of data was determined to be useful if analyzed, while only 5,063 exabytes (EB) of data (47.6%) was analyzed in 2022. How does an open data lakehouse architecture support AI? All of this supports the use of AI.

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Dataversity

DECEMBER 14, 2020

It was only a few years ago that BI and data experts excitedly claimed that petabytes of unstructured data could be brought under control with data pipelines and orderly, efficient data warehouses. But as big data continued to grow and the amount of stored information increased every […].

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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.

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phData

NOVEMBER 8, 2024

Open Table Format (OTF) architecture now provides a solution for efficient data storage, management, and processing while ensuring compatibility across different platforms. In this blog, we will discuss: What is the Open Table format (OTF)? Delta Lake became popular for making data lakes more reliable and easy to manage.

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phData

AUGUST 9, 2023

If you’ve been watching how Snowflake Data Cloud has been growing and changing over the years, you’ll see that two tools have made very large impacts on the Modern Data Stack: Fivetran and dbt. In short, ELT exemplifies the data strategy required in the era of big data, cloud, and agile analytics.

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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. It ensures the data is accurate and reliable, leading to better decision-making.

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Alation

JULY 22, 2021

This course called on the students to utilize the catalog to find and query sample data, and then to publish results into articles on the site. For the course, ‘Big Data and Society’, we loaded publicly available COVID-19 data into the catalog for student use and investigation. iSchool Skills for Data Catalog Management.

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Alation

FEBRUARY 20, 2020

As big data matures, the way you think about it may have to shift also. It’s no longer enough to build the data warehouse. Self-service analytics environments are giving rise to the data marketplace. Self-service analytics environments are giving rise to the data marketplace.

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Alation

JANUARY 17, 2023

It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse. Data ingestion/integration services. Data orchestration tools. In the past, data movement was defined by ETL: extract, transform, and load.

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Alation

FEBRUARY 20, 2020

With the year coming to a close, many look back at the headlines that made major waves in technology and big data – from Spark to Hadoop to trends in data science – the list could go on and on. 2016 will be the year of the “logical data warehouse.” Subscribe to Alation's Blog.

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IBM Journey to AI blog

JULY 11, 2023

Data warehouses are a critical component of any organization’s technology ecosystem. The next generation of IBM Db2 Warehouse brings a host of new capabilities that add cloud object storage support with advanced caching to deliver 4x faster query performance than previously, while cutting storage costs by 34x 1.

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phData

APRIL 20, 2023

In today’s world, data-driven applications demand more flexibility, scalability, and auditability, which traditional data warehouses and modeling approaches lack. This is where the Snowflake Data Cloud and data vault modeling comes in handy. What is Data Vault Modeling?

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IBM Journey to AI blog

JULY 5, 2023

This was, without a question, a significant departure from traditional analytic environments, which often meant vendor-lock in and the inability to work with data at scale. Another unexpected challenge was the introduction of Spark as a processing framework for big data.

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phData

MARCH 7, 2023

From keeping an active backup to consolidating or broadcasting data between platforms, GoldenGate is a very versatile tool that can handle many different use cases. Prerequisites In this blog, we focus on ingesting data into the Snowflake Data Cloud with GoldenGate and so we will pick up the replication process within GoldenGate.

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IBM Journey to AI blog

SEPTEMBER 19, 2023

To pursue a data science career, you need a deep understanding and expansive knowledge of machine learning and AI. And you should have experience working with big data platforms such as Hadoop or Apache Spark. Your skill set should include the ability to write in the programming languages Python, SAS, R and Scala.

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phData

OCTOBER 9, 2023

However, to harness the full potential of Snowflake’s performance capabilities, it is essential to adopt strategies tailored explicitly for data vault modeling. By implementing the best practices and strategies outlined in this blog, organizations can unlock the full potential of their data vault architecture in Snowflake.

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phData

MAY 12, 2023

The integration capabilities of KNIME and the scalable data warehousing of Snowflake combine to offer a flexible and powerful platform for financial data analytics. In this blog, we will delve into three specific use cases where the KNIME-Snowflake combination is effectively deployed in the financial services industry.

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IBM Journey to AI blog

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? One challenge in applying data science is to identify pertinent business issues.

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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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IBM Journey to AI blog

NOVEMBER 3, 2023

However, not all of it is necessarily actionable and some get stuck in queues or big data batch processing. Additionally, Apache Flink contextualizes your data by detecting patterns, enabling you to understand how things happen alongside each other.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

Hosted on Amazon ECS with tasks run on Fargate, this platform streamlines the end-to-end ML workflow, from data ingestion to model deployment. This blog post delves into the details of this MLOps platform, exploring how the integration of these tools facilitates a more efficient and scalable approach to managing ML projects.

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phData

AUGUST 16, 2023

By leveraging Snowflake’s cloud-native architecture and the principles of the Data Vault model, organizations can unlock numerous benefits in terms of scalability, performance, data integrity, and collaborative data sharing. What is a Data Vault Architecture? Using dbt is one of the best choices. Contact phData!

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Dataversity

FEBRUARY 18, 2022

Even as we grow in our ability to extract vital information from big data, the scientific community still faces roadblocks that pose major data mining challenges. In this article, we will discuss 10 key issues that we face in modern data mining and their possible solutions.

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