Bitwise launches new eBook on Data Modernization: A Comprehensive Guide for Data Leaders

November 3, 2023

Chicago, IL – Bitwise, a leading provider of data and analytics solutions, today announced the release of its new eBook on ‘Data Modernization: Cloud-Native Architecture Transformation of ETL, Data Objects and Orchestration’ to assist Chief Data Officers, Analytics VPs, Data Architects and Cloud Architects in the evaluation and decision-making process to take advantage of new possibilities in data modernization.

Cloud-Native architecture has gained significant popularity and adoption in recent years due to its advantages and benefits. This eBook explores the key concepts, benefits, challenges and solutions for transforming traditional ETL, data objects and orchestration with Cloud-Native architecture to enable organizations to build robust and highly available data systems, ensuring success of data modernization initiatives.

The eBook covers the following topics that can help businesses in building data pipelines that meet modern analytics and AI (Artificial Intelligence) requirements:

  • Data Modernization: Data modernization is the process of transforming existing data architectures and infrastructures to meet the needs of the modern enterprise. This includes moving to Cloud-Native architectures, adopting new technologies, and improving data management practices. This eBook provides an overview of the benefits, key components, challenges and risks involved in data modernization projects. 
  • Cloud-Native Architecture: The eBook discusses the key principles of Cloud-Native architecture, such as scalability, reliability, and security. It also covers the benefits of Cloud-Native architecture related to data processing and analytics that are designed to take advantage of the unique capabilities of the cloud, such as elasticity, on-demand provisioning, and pay-as-you-go pricing. 
  • ETL / ELT: ETL (Extract, Transform, and Load) and ELT (Extract, Load, and Transform) are two data integration processes used to extract data from sources, transform, and load into a target destination. In ETL, the data is transformed before it is loaded into the target destination such as a data warehouse. In ELT, the data is loaded into the target destination such as a data lake where it can be transformed any number of times based on user requirements. This eBook addresses how future ETL/ELT data management frameworks will provide a comprehensive and hybrid approach for managing big data.
  • Data Objects: Data objects are the building blocks of data pipelines. They represent the data being extracted, transformed, and loaded into the target data warehouse or data lake. This eBook explains concepts of data objects, types and how they can be transformed for cloud.  
  • Data Orchestration: Data orchestration is the crucial process of coordinating and managing the execution of data pipelines. It ensures that the data is processed in the correct order and the results are delivered to the right users and applications. The aspects of data orchestration, such as workflow management, data quality, and monitoring are discussed in this eBook.

In addition to detailing the benefits and challenges of the various components of data modernization, this eBook presents the Bitwise Data Platform Cloud Migration Suite of automation tools and services to solve challenges of transforming ETL, data objects and orchestration in the cloud. 

“As an organization, we take an automation-first mindset to solve technology challenges,” said Shahab Kamal, Chief Technology Officer at Bitwise. “Our ETL Modernization Practice is based on over 12 years of ETL migration experience in converting over 30,000 ETL applications from one tool to another. We've developed a suite of frameworks and tools – Data Platform Cloud Migration Suite – to accelerate data modernization initiatives by using AI-powered automation that is discussed in the eBook.”

Download this eBook now to explore more on how Cloud-Native architecture transformation helps to meet data modernization goals:

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