How Can a Big Data Engineer Put Big Data
to Work for Your Business?
Data collected by everything from your smartphone to your Wi-Fi-linked dishwasher is easy to overlook. By 2025, the globe is expected to have generated and stored 200 Zettabytes of data. While storing this much data is a task in and of itself, extracting value from this much data is substantially more difficult.
You've probably heard of "Big Data," and this industry's size is only increasing. By enhancing data accessibility by 10%, Fortune 1000 businesses can achieve more than $65 million in additional net income. Specifically, organizations need to hire employees with data-related skills, such as data engineers, to accomplish this.
A Data Flow Diagram (DFD) is a traditional visual representation of the information flows within a system. DFD can be useful for big data engineering in your business.
Nobody will keep up with the pace of technological change. It's impossible to know it all. If you enroll in an online data engineer bootcamp or sign up for online courses, you can remain ahead and become a big data engineer from the comfort of your own home. Read on to learn more about Big Data Engineers, what they do, and why they're so crucial to the success of your organization.
What is the job of a Data Engineer?
Big Data Engineers design and build data processing systems capable of managing massive amounts of data. Data engineers can fulfill many tasks and duties, often representing one or more of the essential aspects of data engineering from the above list.
Your organization's specific needs will dictate the role of a data engineer. A data engineer is responsible for storing, extracting, transforming, loading, aggregating, and validating the data, they work with. It entails:
- They build data pipelines and store the data in a way that is efficient for tools that need to query it.
- They analyze and assure compliance with all applicable data governance rules and regulations.
- They learn about the advantages and disadvantages of various data storage and query methods.
Use AWS to store and query data from several systems, say, in the case of an organization that uses AWS as its cloud service provider. Is your data key or value-oriented? If so, do the data have complex relationships? Does the data have to be processed or merged with other data sets? Decisions made by a data engineer will impact how the data will ingest, processed, and archive in the future.
On the other hand, a big data engineer must deal with big data uniquely. Take a closer look at these things.
1. Performance optimization
Big data platforms necessitate a high level of performance. Engineers in big data must keep an eye on the entire process and make required infrastructure adjustments to speed up query execution. The following are examples of what you can do.
- Database optimization techniques: There are a few ways to break and store data into separate, self-contained chunks. One of these is data partitioning. Fast lookups are made possible by assigning each data block a unique partition key. Another option is database indexing to speed up data retrieval in massive databases. Data denormalization is a technique used by big data engineers to reduce the number of tables that need to be joined by adding duplicate data.
- Efficient data ingestion: It becomes more challenging to convey data in various forms continually accelerated. Big data engineers can increase the volume of data in the data lake by employing data mining techniques and different data ingestion APIs.
2. Stream processing
One of the most typical tasks for big data engineers in setting up and managing streaming data flows. Companies are making extensive use of IoT devices, transactional data, and physical sensors in today's world. The weird thing about data streams is that they are a never-ending stream of changes quickly out of date. As a result, the processing of such data is time-sensitive. It is a situation where a typical batch processing strategy is not appropriate. When working with large datasets, Big Data engineers provide event stream processors with data streams to simultaneously process data, update it, and deliver it to the user.
3. Deploying ML models
Data scientists who cannot write production-ready code and construct it in the pipeline are frequently forced to turn to big data engineers for help, even though this is not their core role. For example, we need to classify streaming photos in the pipeline before storing them. A big data engineer will have to implement an appropriate machine learning model in the data pipeline whenever this occurs.
As a Data Engineer, how do you add value?
Big data engineers are often involved in the deployment process to ensure production-ready code is included in the pipeline, like storing streaming photos first. So a prominent data engineer must deploy an ML model.
It will likely involve data from your ERP system, supply chain system, third-party providers, and internal company structure. Historically, some firms may have attempted to construct this report in Excel, involving numerous business analysts and engineers.
Data engineers help businesses collect data from numerous sources and store it in a data lake or several Kafka topics. After each system's data is acquired, a data engineer can best decide how to combine the data sets.
Then data engineers can create data pipelines to allow data to flow from source systems. The output of this data pipeline is subsequently stored somewhere else, usually in a representative format for various business intelligence tools.
Data engineers must also ensure that data pipelines have valid inputs and outputs. A data pipeline to validate against the source systems is frequently required. Data engineers must also maintain data pipelines and use various monitoring tools and SRE procedures to keep the information current.
In a nutshell, data engineers provide value by automating and optimizing complicated systems, making data a valuable corporate asset.
A big data engineer is one of the most sought-after designations in the age of Big Data. According to DICE's 2020 Tech Job Report, 'data engineer' is the fastest-growing tech occupation, with a 50% increase in job postings year on year.
With the Data Engineering professional certificates, you may gain job-ready skills for careers in data engineering.