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Big Data Engineer vs. Data Warehouse Engineer

by Ramit kaur
Big Data Engineer vs. Data Warehouse Engineer

The Data Warehouse Engineer is in charge of overseeing the back-end development of its data warehouse during its entire life cycle. The Data Warehouse Engineer is in charge of ETL process creation, database and performance management cube development, and table structure dimensional design.

When it comes to Big Data, the concept can be extremely perplexing. What constitutes “big data,” and what does not?

While large data is still data, it necessitates a different technical approach for a variety of reasons, and one of them being its magnitude. Big data is a vast amount of unstructured, heterogeneous data that continues to accumulate at a rapid rate. As a result, standard data transfer technologies are unable to manage the massive data flow effectively. The creation of new methods for transferring, storing, and analyzing vast amounts of unstructured data is aided by big data.

There are many Data engineering courses in India that help in building a career in this field prominently. 

Responsibilities of Data Warehouse:

Management: The Data Warehouse Engineer is a manager that oversees the data warehouse’s day-to-day operations and troubleshoots existing procedures and processes. He defines and promotes the department’s best practices and design concepts for data warehousing techniques and architecture. In addition, the Data Warehouse Engineer works to improve data organization and accuracy. He assists in developing business intelligence, business data standards, processes, monitors, and troubleshoots performance issues on data warehouse servers.

Design/Strategy: The Data Warehouse Engineer creates and maintains the data warehouse’s database and table schemas for new and existing data sources. He also constructs and maintains the ETL, which allows data to be accommodated into the warehouse via SSIS and other technologies. The Data Warehouse Engineer’s job entails designing and developing systems for the company’s data warehouse, ETL procedures, and business intelligence.

Analytics: The Data Warehouse Engineer is an analytical role player who analyses business requirements for reporting and analysis fast and comprehensively, then translates the results into appropriate technical data architectures. The Data Warehouse Engineer establishes report documentation, develops technical specification documentation for all reports and procedures, and maintains it.

Collaboration: Data Warehouse Engineers work together with data analysts, data scientists, and other data consumers to acquire and populate data warehouse table structures optimized for reporting. In addition, the Data Warehouse Engineer works with other disciplines, departments, and teams inside the organization to deliver simple, functional, and elegant solutions that balance data needs across the board.

Knowledge: The Data Warehouse Engineer is also responsible for collecting and maintaining best practices for big data stacking and sharing within the organization. The Data Warehouse Engineer contributes to the company’s data analysis, reporting, data warehousing, and business intelligence efforts. The Data Warehouse Engineer’s responsibilities include providing technical expertise on business intelligence data architecture and structured techniques for transferring manual applications and reports to the business. This is one of the most major responsibilities which has been taught in data engineering courses in India. 

Interpersonal Skills: The Data Warehouse Engineer must have a positive can-do attitude, be open to change, be a self-starter and self-motivated individual, be proactive and go above and beyond the call of duty, take responsibility for business performance, have innovative problem-solving skills, be a creative and strategic thinker, and have a strong work ethic.

People Skills: A Data Warehouse Engineer must form strong, meaningful, and long-term relationships with others. He will be likable, generating trust and a sense of reliability in others, providing his thoughts and directives, more credibility in the eyes of collaborating workers, senior management, and his peers.

Other Duties: The Data Warehouse Engineer performs other duties as assigned by the Senior Data Warehouse Engineer, Head of Analytics, Director Analytics, Chief Data Officer, or the Employer for the proper execution of his duties and responsibilities.

Responsibilities of Data Engineer:

However, when working with big data, a big data engineer’s tasks are unique. Let’s have a look at them, which are learned during the data engineering courses in India. 

Enhancement of performance: When it comes to large data platforms, performance is known to be crucial. Big data engineers should monitor the entire process and make needed infrastructure adjustments to speed up the query execution. It includes the use of the following items.

Techniques for improving database performance: Data partitioning is one of them, which involves dividing and storing data into independent, self-contained portions. Each data block is given a partition key for convenient lookup. Another approach for organizing data to speed up data retrieval procedures in large tables is database indexing. Big data engineers employ denormalization to reduce the number of table joins by introducing redundant data to one or more tables.

Let us clear your doubt about whose number is this calling me.

Quick and easy Data ingestion: Things get a little more tricky when transferring data in various formats at a high rate. By using data mining techniques and other data ingestion APIs to detect patterns in data sets, big data engineers may capture and inject more data into the data lake.

Stream processing: Setting up and managing streaming flows is one of the most common jobs for big data engineers. Businesses make substantial use of transactional data, IoT devices, and physical sensors. The continual flow of changes that quickly lose their importance is what distinguishes data streams. 

In this scenario, a normal batch processing method will not be sufficient, and there isn’t enough time to store data streams and then process them later. It uses a distinct approach, allowing many streams to be handled simultaneously. Event stream processors process data in real-time, keep it updated, and offer it to users regularly, thanks to big data engineers feeding data streams to them.

Deploying ML models: A big data engineer is commonly involved in the deployment process if a data scientist cannot produce production-ready code and put it in the pipeline. For example, we have streaming photographs that need to be classified in the pipeline before being stored. In this case, a big data engineer must install an ML model that correlates to the data pipeline’s data.

These are some of the differences between data warehouse engineers and data engineers. Now, do not delay anymore and join in any of the data engineering courses in India. 

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