Data Science 101

NOVEMBER 7, 2019

Microsoft just held one of its largest conferences of the year, and a few major announcements were made which pertain to the cloud data science world. Azure Synapse. Azure Synapse Analytics can be seen as a merge of Azure SQL Data Warehouse and Azure Data Lake. Azure Quantum.

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Data Science Azure SQL Machine Learning 106

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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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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Data Warehouse Data Lakes Azure AWS 147

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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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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Data Science Blog

SEPTEMBER 19, 2023

By automating the provisioning and management of cloud resources through code, IaC brings a host of advantages to the development and maintenance of Data Warehouse Systems in the cloud. So why using IaC for Cloud Data Infrastructures? Of course, Terraform and the Azure CLI needs to be installed before.

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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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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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Data Quality Cloud Data Data Observability Data Pipeline 52

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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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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Azure Database Cloud Computing AWS 52

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.

Analytics 203

Analytics Analytics Big Data Analytics Big Data Analytics 203

Data Science Blog

FEBRUARY 4, 2023

The fusion of data in a central platform enables smooth analysis to optimize processes and increase business efficiency in the world of Industry 4.0 using methods from business intelligence , process mining and data science. Cloud Data Platform for shopfloor management and data sources such like MES, ERP, PLM and machine data.

Cloud Data 147

Cloud Data Data Science Business Intelligence Business Intelligence 147

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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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

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

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

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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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 90

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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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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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.

Cloud Computing 110

Cloud Computing Azure Cloud Data Database 110

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

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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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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IBM Journey to AI blog

AUGUST 3, 2023

By analyzing traffic on an autonomous and continuous basis—as well as data repositories connected to the network—IBM Security Discover and Classify can detect all elements on the network that are storing, processing and sharing sensitive data both outside and inside the network.

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