Data Engineering & Architecture
Data is a life blood for business success.
Data Engineering and Architecture involves the practice to use a set of business rules to clean and organize the raw data to make it useful as it born according the
requirements to answer business queries and to prepare it for storage, data analytics and machine learning.
The Power Of Data
Data Driven Decisions Entail Modern Data Architecture
Data driven decision for a business in no more optional, its mendatory now. The data architecture emerges as an indispensable asset for enterprises
striving to remain agile,
competitive, and truly data-driven. By embracing a modern and smooth data architecture companies can position themselves to not only address their present data needs but also to seamlessly adapt to the ever-changing technological and market landscape, ensuring their long-term success.
Multiple Data Types
With emergence of technologies and social media usage, multiple data types are generated from different sources.
It includes structured, unstructured, and semi structured data.
The data from different data types need to be process, clean and transform programmatically for storage
and further use of AI and analytics to determine bueiness policies.
Therefore organizations are seeking to adopt modern data architecture to meet their storage requirements according their
needs of analytics and to adopt the AI in organizations.
Analytics and AI Ready Data
Data Analytics required refined, transformed,cleaned and processed data to create valuable outputs that unlock business insights
and answer business queries. Modern data architecture ensure smooth data storage and availability for further processing. The data preparation
for analytics involves a complex procedure to extract, transformed and load process, data architecture plays a basic role for this data preparation.
Reporting Availability
The main purpose of data analytics is to drive valuable insights from data and to take actionable decisions to determine business future. Data Architecture detects the stakeholders who will use that data and their distinctive requirements for all stack holders marketing team, finance department, sales department. All stack holders can easily access their required data for their actionable decisions.
ETL Pipelines
Organizations are generating multiple types of data from multiple sources.
It incereased the need of initiatives to generate business insights for analytics, data science and machine learning.
Prioritize thes initiatives are critical because it is crucial to translate raw data into useful information.
ETL piplines are used to extract, transform and load from multiple sources programmatically into usable and trusted resource to solve business problem
and to answer business queries for all involved stack holders.
Stream Processing
Stream processing is a practice of taking action on data as it born. Stream processing often applied to the data that is generated as a result of any event like payment processing, order confirmation, server and application logs. Data is generated as a result of some event and delivered to a stream processing application, where the data may be augmented, tested against fraud detection algorithms, or transformed for specific requirements.
Visualize Results
Data visualization presents all results to specific user according their requirements. Data visualization make it easy to understand for non technical users current business position, what happned in past and what are expected results in coming future. The decision makers can determine the effective measures to take for future business on the base of current and previous factual results.
