Data Science Dojo

JULY 6, 2023

Data engineering tools offer a range of features and functionalities, including data integration, data transformation, data quality management, workflow orchestration, and data visualization. Essential data engineering tools for 2023 Top 10 data engineering tools to watch out for in 2023 1.

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Data Science Dojo

FEBRUARY 1, 2023

Introduction Dedicated SQL pools offer fast and reliable data import and analysis, allowing businesses to access accurate insights while optimizing performance and reducing costs. DWUs (Data Warehouse Units) can customize resources and optimize performance and costs.

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Flipboard

JUNE 17, 2025

Organizations manage extensive structured data in databases and data warehouses. Large language models (LLMs) have transformed natural language processing (NLP), yet converting conversational queries into structured data analysis remains complex. For this post, we demonstrate the setup option with IAM access.

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Dataconomy

FEBRUARY 27, 2025

Its ability to scale efficiently has allowed companies to harness the insights locked within their data, paving the way for enhanced analytics, predictive insights, and innovative applications across various industries. Hadoop is an open-source framework that supports distributed data processing across clusters of computers.

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Women in Big Data

NOVEMBER 27, 2024

A data warehouse is a centralized repository designed to store and manage vast amounts of structured and semi-structured data from multiple sources, facilitating efficient reporting and analysis. Begin by determining your data volume, variety, and the performance expectations for querying and reporting.

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Smart Data Collective

APRIL 8, 2020

Dating back to the 1970s, the data warehousing market emerged when computer scientist Bill Inmon first coined the term ‘data warehouse’. Created as on-premise servers, the early data warehouses were built to perform on just a gigabyte scale. The post How Will The Cloud Impact Data Warehousing Technologies?

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phData

FEBRUARY 21, 2025

In this post, we will be particularly interested in the impact that cloud computing left on the modern data warehouse. We will explore the different options for data warehousing and how you can leverage this information to make the right decisions for your organization. Understanding the Basics What is a Data Warehouse?

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Dataconomy

MARCH 4, 2025

The data mining process The data mining process is structured into four primary stages: data gathering, data preparation, data mining, and data analysis and interpretation. Each stage is crucial for deriving meaningful insights from data.

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AWS Machine Learning Blog

JULY 17, 2023

Amazon Redshift is a fully managed, fast, secure, and scalable cloud data warehouse. Organizations often want to use SageMaker Studio to get predictions from data stored in a data warehouse such as Amazon Redshift. This should return the records successfully for further data processing and analysis.

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Pickl AI

JULY 29, 2024

Summary: A Hadoop cluster is a collection of interconnected nodes that work together to store and process large datasets using the Hadoop framework. 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.

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phData

NOVEMBER 9, 2023

What Components Make up the Snowflake Data Cloud? This data mesh strategy combined with the end consumers of your data cloud enables your business to scale effectively, securely, and reliably without sacrificing speed-to-market. What is a Cloud Data Warehouse? Today, data lakes and data warehouses are colliding.

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Tableau

JUNE 8, 2021

Domain experts, for example, feel they are still overly reliant on core IT to access the data assets they need to make effective business decisions. In all of these conversations there is a sense of inertia: Data warehouses and data lakes feel cumbersome and data pipelines just aren't agile enough.

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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.

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phData

JUNE 11, 2025

Whether you’re running small-scale analytics or managing enterprise-level data warehouses, these tips will help drive performance and meaningful business outcomes for your organization. Storage Costs Our first tip involves taking a closer look at managing how your data is stored, organized, and accessed.

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Dataconomy

MAY 26, 2025

Supported platforms Azure Data Studio is compatible with: Windows Linux macOS It supports SQL Server (2014 and later), Azure SQL Database, and Azure SQL Data Warehouse, making it a versatile choice for a range of database environments. This feature is especially useful for working with SQL Server 2019’s big data clusters.

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Smart Data Collective

MAY 16, 2022

These include, but are not limited to, database management systems, data mining software, decision support systems, knowledge management systems, data warehousing, and enterprise data warehouses. Some data management strategies are in-house and others are outsourced.

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AWS Machine Learning Blog

DECEMBER 29, 2022

Many of the RStudio on SageMaker users are also users of Amazon Redshift , a fully managed, petabyte-scale, massively parallel data warehouse for data storage and analytical workloads. It makes it fast, simple, and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools.

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Flipboard

AUGUST 17, 2023

Amazon Redshift is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. Here we use RedshiftDatasetDefinition to retrieve the dataset from the Redshift cluster. We attached the IAM role to the Redshift cluster that we created earlier.

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Smart Data Collective

JUNE 16, 2022

The data is processed and modified after it has been extracted. Data is fed into an Analytical server (or OLAP cube), which calculates information ahead of time for later analysis. A data warehouse extracts data from a variety of sources and formats, including text files, excel sheets, multimedia files, and so on.

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IBM Data Science in Practice

JANUARY 13, 2025

It is a cloud-native approach, and it suits a small team that does not want to host, maintain, and operate a Kubernetes cluster alonewith all the resulting responsibilities (and costs). The blog post explains how the Internal Cloud Analytics team leveraged cloud resources like Code-Engine to improve, refine, and scale the data pipelines.

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phData

NOVEMBER 8, 2024

Versioning also ensures a safer experimentation environment, where data scientists can test new models or hypotheses on historical data snapshots without impacting live data. Note : Cloud Data warehouses like Snowflake and Big Query already have a default time travel feature. FAQs What is a Data Lakehouse?

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phData

OCTOBER 9, 2023

Understanding Data Vault Modeling Created in the 1990s by a team at Lockheed Martin, data vault modeling is a hybrid approach that combines traditional relational data warehouse models with newer big data architectures to build a data warehouse for enterprise-scale analytics.

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Smart Data Collective

OCTOBER 13, 2021

Some solutions provide read and write access to any type of source and information, advanced integration, security capabilities and metadata management that help achieve virtual and high-performance Data Services in real-time, cache or batch mode. How does Data Virtualization complement Data Warehousing and SOA Architectures?

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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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phData

MAY 12, 2023

.” This is where you might think about data clustering to increase throughput and decrease latency for your queries. In this blog, we will explore the option of data clustering. What is Clustering Data in Snowflake? A simple example would be to cluster on a date or timestamp column.

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Clustering Database Data Warehouse Analytics 64

Tableau

JUNE 8, 2021

Domain experts, for example, feel they are still overly reliant on core IT to access the data assets they need to make effective business decisions. In all of these conversations there is a sense of inertia: Data warehouses and data lakes feel cumbersome and data pipelines just aren't agile enough.

Tableau 52

Tableau Data Lakes Data Warehouse SQL 52

Mlearning.ai

MAY 16, 2023

It is used to extract data from various sources, transform the data to fit a specific data model or schema, and then load the transformed data into a target system such as a data warehouse or a database. In the extraction phase, the data is collected from various sources and brought into a staging area.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Mlearning.ai

FEBRUARY 16, 2023

The ultimate need for vast storage spaces manifests in data warehouses: specialized systems that aggregate data coming from numerous sources for centralized management and consistency. In this article, you’ll discover what a Snowflake data warehouse is, its pros and cons, and how to employ it efficiently.

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ODSC - Open Data Science

MARCH 7, 2025

European Central Bank (ECB) Statistical Data Warehouse Source: ECB Features: Interest rates, inflation, monetary policy indicators Use Cases: Macro-financial analysis, policy forecasting Access: Free API and CSV downloads 10. Feature Engineering: Identify key indicators and create meaningful features for predictive models.

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IBM Journey to AI blog

SEPTEMBER 25, 2023

When a query is constructed, it passes through a cost-based optimizer, then data is accessed through connectors, cached for performance and analyzed across a series of servers in a cluster. Because of its distributed nature, Presto scales for petabytes and exabytes of data.

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Data Lakes Analytics Analytics Clustering 102

AWS Machine Learning Blog

FEBRUARY 14, 2024

Amazon Redshift has announced a feature called Amazon Redshift ML that makes it straightforward for data analysts and database developers to create, train, and apply machine learning (ML) models using familiar SQL commands in Redshift data warehouses. It may take a few minutes for the access status to change to Access granted.

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AWS Machine Learning Blog

NOVEMBER 1, 2023

PII Detected tagged documents are fed into Logikcull’s search index cluster for their users to quickly identify documents that contain PII entities. The request is handled by Logikcull’s application servers hosted on Amazon EC2 and the servers communicates with the search index cluster to find the documents.

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Pickl AI

FEBRUARY 23, 2025

Apache Hadoop Apache Hadoop is an open-source framework that allows for distributed storage and processing of large datasets across clusters of computers using simple programming models. Key Features : Scalability : Hadoop can handle petabytes of data by adding more nodes to the cluster. Statistics Kafka handles over 1.1

Big Data 52

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phData

MAY 12, 2023

KNIME and Snowflake work together to create a seamless data analytics pipeline. It starts with KNIME, which can directly connect to your Snowflake data warehouse using its dedicated database Snowflake connector node. KNIME can then connect to this Snowflake data warehouse and extract the necessary data for risk assessment.

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phData

AUGUST 16, 2024

Managing extremely large datasets, complex queries, and varying workloads in a data warehouse can be both challenging and costly. With the Snowflake AI Data Cloud , you can adjust compute resource levels on a virtual warehouse. It is already optimized using data clustering and micro-partitions.

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IBM Journey to AI blog

JULY 5, 2023

It gained rapid popularity given its support for data transformations, streaming and SQL. But it never co-existed amicably within existing data lake environments. As a result, it often led to additional dedicated compute clusters just to be able to run Spark.

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phData

JULY 12, 2023

Understanding Data Vault Architecture Data vault architecture is a data modeling and data integration approach that aims to provide a scalable and flexible foundation for building data warehouses and analytical systems. Pictured below is an example of a simple PIT table with a cluster key.

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