Remove Data Processing Remove Data Warehouse Remove Machine Learning Remove Structured Data
article thumbnail

How Will The Cloud Impact Data Warehousing Technologies?

Smart Data Collective

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. Cloud based solutions are the future of the data warehousing market.

article thumbnail

5 Pain Points of Moving Data to the Cloud and Strategies for Success

Alation

We have seen the COVID-19 pandemic accelerate the timetable of cloud data migration , as companies evolve from the traditional data warehouse to a data cloud, which can host a cloud computing environment. Accompanying this acceleration is the increasing complexity of data. Fern Halper, Ph.D.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Migrate your existing SQL-based ETL workload to an AWS serverless ETL infrastructure using AWS Glue

AWS Big Data

Customers often use many SQL scripts to select and transform the data in relational databases hosted either in an on-premises environment or on AWS and use custom workflows to manage their ETL. AWS Glue is a serverless data integration and ETL service with the ability to scale on demand.

Sales 52
article thumbnail

Quantitative and Qualitative Data: A Vital Combination

Sisense

Most commonly, we think of data as numbers that show information such as sales figures, marketing data, payroll totals, financial statistics, and other data that can be counted and measured objectively. This is quantitative data. It’s “hard,” structured data that answers questions such as “how many?”

article thumbnail

Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

This can be achieved using AWS Entity Resolution , which enables using rules and machine learning (ML) techniques to match records and resolve identities. The AWS modern data architecture shows a way to build a purpose-built, secure, and scalable data platform in the cloud.

article thumbnail

Run Apache Hive workloads using Spark SQL with Amazon EMR on EKS

AWS Big Data

Apache Hive is a distributed, fault-tolerant data warehouse system that enables analytics at a massive scale. Spark SQL is an Apache Spark module for structured data processing. host') export PASSWORD=$(aws secretsmanager get-secret-value --secret-id $secret_name --query SecretString --output text | jq -r '.password')