Analytics Vidhya

OCTOBER 25, 2023

Introduction Struggling with expanding a business database due to storage, management, and data accessibility issues? To steer growth, employ effective data management strategies and tools. This article explores data management’s key tool features and lists the top tools for 2023.

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IBM Journey to AI blog

FEBRUARY 2, 2023

Companies are shifting their investments to cloud software and reducing their spend on legacy infrastructure. In 2021, cloud databases accounted for 85% 1 of the market growth in databases. What is holding back the other 50% of datasets on-premises?

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Data Science Blog

NOVEMBER 15, 2023

In addition to Business Intelligence (BI), Process Mining is no longer a new phenomenon, but almost all larger companies are conducting this data-driven process analysis in their organization. The Event Log Data Model for Process Mining Process Mining as an analytical system can very well be imagined as an iceberg. Click to enlarge!

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Data Modeling Data Models Business Intelligence Business Intelligence 245

The Data Administration Newsletter

JANUARY 20, 2021

In the cloud-era, should you store your corporate data in Cosmos DB on Azure, Cloud Spanner on the Google Cloud Platform, or in the Amazon Quantum Ledger? However, they […].

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IBM Journey to AI blog

MAY 12, 2023

IBM’s recommendations included API-specific improvements, bot UX optimization, workflow optimization, DevOps microservices and design consideration, and best practices for Azure manage services.

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Data Science Blog

MAY 15, 2023

Data Lakehouses werden auf Cloud-basierten Objektspeichern wie Amazon S3 , Google Cloud Storage oder Azure Blob Storage aufgebaut. In einem Data Lakehouse werden die Daten in ihrem Rohformat gespeichert, und Transformationen und Datenverarbeitung werden je nach Bedarf durchgeführt. So basieren z.

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Pickl AI

DECEMBER 26, 2024

As the global cloud computing market is projected to grow from USD 626.4 Defining Cloud Computing in Data Science Cloud computing provides on-demand access to computing resources such as servers, storage, databases, and software over the Internet. billion in 2023 to USD 1,266.4

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Smart Data Collective

APRIL 29, 2020

Usually the term refers to the practices, techniques and tools that allow access and delivery through different fields and data structures in an organisation. Data management approaches are varied and may be categorised in the following: Cloud data management. Master data management. Microsoft Azure.

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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.

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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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IBM Journey to AI blog

SEPTEMBER 11, 2023

Thus, was born a single database and the relational model for transactions and business intelligence. Its early success, coupled with IBM WebSphere in the 1990s, put it in the spotlight as the database system for several Olympic games, including 1992 Barcelona, 1996 Atlanta, and the 1998 Winter Olympics in Nagano.

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Database SQL Data Warehouse Machine Learning 101

Dataconomy

SEPTEMBER 4, 2023

Cloud-based business intelligence (BI): Cloud-based BI tools enable organizations to access and analyze data from cloud-based sources and on-premises databases. These tools offer the flexibility of accessing insights from anywhere, and they often integrate with other cloud analytics solutions.

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phData

MAY 25, 2023

Having gone public in 2020 with the largest tech IPO in history, Snowflake continues to grow rapidly as organizations move to the cloud for their data warehousing needs. In a perfect world, Microsoft would have clients push even more storage and compute to its Azure Synapse platform.

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Power BI Analytics Analytics Azure 84

Flipboard

MARCH 22, 2023

In this post, we show how to configure a new OAuth-based authentication feature for using Snowflake in Amazon SageMaker Data Wrangler. Snowflake is a cloud data platform that provides data solutions for data warehousing to data science. For Azure AD, you must also specify a unique identifier for the scope.

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phData

APRIL 29, 2024

Recognizing these specific needs, Fivetran has developed a range of connectors, including dedicated applications, databases, files, and events, which can accommodate the diverse formats used by healthcare systems. Addressing these needs may pose challenges that lead to the implementation of custom solutions rather than a uniform approach.

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Smart Data Collective

OCTOBER 16, 2022

Key features of the backend cloud architecture: The backend is responsible for monitoring all the programs that run the application on the front end. It has a large number of servers and data storage systems and is an essential part of the entire cloud infrastructure. Storage for storing and maintaining data over the network.

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phData

FEBRUARY 14, 2023

Data integration is essentially the Extract and Load portion of the Extract, Load, and Transform (ELT) process. Data ingestion involves connecting your data sources, including databases, flat files, streaming data, etc, to your data warehouse. Snowflake provides native ways for data ingestion.

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phData

SEPTEMBER 2, 2024

In this blog, we will cover the best practices for developing jobs in Matillion, an ETL/ELT tool built specifically for cloud database platforms. Matillion is a SaaS-based data integration platform that can be hosted in AWS, Azure, or GCP. Database names, Cloud Region, etc.

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Pickl AI

OCTOBER 15, 2024

This process enables businesses to consolidate data from different platforms, ensuring it’s ready for analysis and decision-making. The first step in the ETL process is extraction, where data is gathered from different sources, such as databases, cloud services, or flat files.

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Precisely

MARCH 14, 2023

First, private cloud infrastructure providers like Amazon (AWS), Microsoft (Azure), and Google (GCP) began by offering more cost-effective and elastic resources for fast access to infrastructure. Simply design data pipelines, point them to the cloud environment, and execute. So how did providers respond?

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Mlearning.ai

FEBRUARY 16, 2023

The solution was built on top of Amazon Web Services and is now available on Google Cloud and Microsoft Azure. Therefore, the tool is referred to as cloud-agnostic. Snowflake Database Pros Extensive Storage Opportunities Snowflake provides affordability, scalability, and a user-friendly interface. What does Snowflake do?

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Data Warehouse Business Intelligence Business Intelligence Database 52

phData

DECEMBER 4, 2023

However, if there’s one thing we’ve learned from years of successful cloud data implementations here at phData, it’s the importance of: Defining and implementing processes Building automation, and Performing configuration …even before you create the first user account. One day is usually adequate for development use.

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Database Clustering SQL Data Pipeline 98

phData

MAY 11, 2023

There are many frameworks for testing software, but the right way to test the data and SQL scripts that change data are less obvious. This is because databases and the data therein are constantly changing. Consider the scenario where you create a view in the database using your Development (DEV) environment.

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phData

JUNE 3, 2024

Snowflake AI Data Cloud has become a premier cloud data warehousing solution. Maybe you’re just getting started looking into a cloud solution for your organization, or maybe you’ve already got Snowflake and are wondering what features you’re missing out on.

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Pickl AI

JULY 12, 2023

It offers easier access to the data and complete restoration and backup. There are several service providers in his domain, like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). The next segment highlights the key benefits of cloud migration and its key features.

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Pickl AI

JANUARY 31, 2025

What is Cloud Computing? Cloud computing refers to the delivery of computing servicesincluding servers, storage, databases, networking, software, analytics, and intelligenceover the Internet (the cloud). Examples include Amazon Web Services (AWS) EC2 and Microsoft Azure.

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Alation

APRIL 14, 2022

This two-part series will explore how data discovery, fragmented data governance , ongoing data drift, and the need for ML explainability can all be overcome with a data catalog for accurate data and metadata record keeping. The Cloud Data Migration Challenge. Data pipeline orchestration.

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Mlearning.ai

JANUARY 26, 2023

EO data is not yet a commodity and neither is environmental information, which has led to a fragmented data space defined by a seemingly endless production of new tools and services that can’t interoperate and aren’t accessible by people outside of the deep tech community ( read more ). Data Intelligence , 2 (1–2), 199–207.

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phData

SEPTEMBER 20, 2023

Database Per Domain A popular approach is to utilize a single Snowflake account. In this setup, various domains operate within distinct databases and autonomous compute clusters, each serving as its independent environment. Disaster recovery is simpler as it only requires one other account in another region or cloud to support.

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phData

AUGUST 6, 2024

Organizations must ensure their data pipelines are well designed and implemented to achieve this, especially as their engagement with cloud data platforms such as the Snowflake Data Cloud grows. For customers in Snowflake, Snowpark is a powerful tool for building these effective and scalable data pipelines.

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phData

MAY 4, 2023

In this blog, we’ll explore the compelling reasons behind transitioning from Teradata to the cutting-edge Snowflake Data Cloud. Teradata was founded in 1979, and it was a revolutionary DBMS (Database Management System) capable of parallel processing with more than one processor at the same time. What is Teradata?

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SQL Data Warehouse Azure Big Data 52

The MLOps Blog

MAY 17, 2023

Cloud ETL Pipeline: Cloud ETL pipeline for ML involves using cloud-based services to extract, transform, and load data into an ML system for training and deployment. Cloud providers such as AWS, Microsoft Azure, and GCP offer a range of tools and services that can be used to build these pipelines.

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ODSC - Open Data Science

JULY 11, 2023

Co-location data centers: These are data centers that are owned and operated by third-party providers and are used to house the IT equipment of multiple organizations. Edge data centers: These are data centers that are located closer to the edge of the network, where data is generated and consumed, rather than in central locations.

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phData

SEPTEMBER 27, 2024

. “ This sounds great in theory, but how does it work in practice with customer data or something like a ‘composable CDP’? Well, implementing transitional modeling does require a shift in how we think about and work with customer data. It often involves specialized databases designed to handle this kind of atomic, temporal data.

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phData

OCTOBER 28, 2024

Understanding Matillion and Snowflake, the Python Component, and Why it is Used Matillion is a SaaS-based data integration platform that can be hosted in AWS, Azure, or GCP and supports multiple cloud data warehouses. Jython is to be used for database connectivity only. The default value is Python3.

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Women in Big Data

DECEMBER 9, 2024

Microsoft Power BI – Power BI is a comprehensive suite of tools which allows you to visualize data and create interactive reports and dashboards. Tableau – Tableau is celebrated for its advanced data visualization and interactive dashboard features. You can also share insights across organizations.

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phData

JULY 18, 2023

With the birth of cloud data warehouses, data applications, and generative AI , processing large volumes of data faster and cheaper is more approachable and desired than ever. First up, let’s dive into the foundation of every Modern Data Stack, a cloud-based data warehouse.

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