Remove Business Objectives Remove Data Analytics Remove Data Transformation
article thumbnail

Migrate Amazon Redshift from DC2 to RA3 to accommodate increasing data volumes and analytics demands

AWS Big Data

With the ever-increasing volume of data available, Dafiti faces the challenge of effectively managing and extracting valuable insights from this vast pool of information to gain a competitive edge and make data-driven decisions that align with company business objectives.

Data Lake 111
article thumbnail

How SOCAR handles large IoT data with Amazon MSK and Amazon ElastiCache for Redis

AWS Big Data

Therefore, SOCAR looked for purpose-built databases tailored to the needs of their application and usage patterns while meeting the future requirements of SOCAR’s business and technical requirements. The following diagram shows an example of data transformations in the handler component.

IoT 111
Insiders

Sign Up for our Newsletter

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

article thumbnail

Building Better Data Models to Unlock Next-Level Intelligence

Sisense

You can’t talk about data analytics without talking about data modeling. These two functions are nearly inseparable as we move further into a world of analytics that blends sources of varying volume, variety, veracity, and velocity. Big data analytics case study: SkullCandy.

article thumbnail

Data Mesh 101: How Data Mesh Helps Organizations Be Data-Driven and Achieve Velocity

Ontotext

This is especially beneficial when teams need to increase data product velocity with trust and data quality, reduce communication costs, and help data solutions align with business objectives. Rather, they become part of the self-serve platform supporting data mesh for the storage and compute needs of each node.

article thumbnail

Unlock scalability, cost-efficiency, and faster insights with large-scale data migration to Amazon Redshift

AWS Big Data

However, you might face significant challenges when planning for a large-scale data warehouse migration. Additionally, organizations must carefully consider factors such as cost implications, security and compliance requirements, change management processes, and the potential disruption to existing business operations during the migration.

article thumbnail

Data Landscape – Navigating The Data Jungle

Anmut

We could give many answers, but they all centre on the same root cause: most data leaders focus on flashy technology and symptomatic fixes instead of approaching data transformation in a way that addresses the root causes of data problems and leads to tangible results and business success. And that’s important.

ROI 52
article thumbnail

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

AWS Big Data

This concludes creating data sources on the AWS Glue job canvas. Next, we add transformations by combining data from these different tables. Transform the data Complete the following steps to add data transformations: On the AWS Glue job canvas, choose the plus sign. Sumitha AP is a Sr.

Sales 52