Store, organize, and process your data efficiently
In the past, a lot of companies who opted for digital transformation produced unimaginable volumes of new types of data. They deployed costly enterprise data warehouses along with data marts to store, process, and analyze it and somehow got success, but, unfortunately, they didn’t achieve distributed, scalable, and reliable IT infrastructure and expertise primarily because in this type of enterprise data architecture, high costs played a critical role and overshadowed all the potential benefits that these businesses achieved. But, today a far better option exists- Data Engineering.
Let me take you through the basics of Data Engineering. Simply, focus on the word “engineering”. As you know engineers design and build things, likewise Data engineers design and build data pipelines that can transform and transport data in various formats. By the time this data reaches the end-users, it becomes highly usable & optimized. These pipelines take data from many disparate sources and collect it into a single warehouse that represents the data uniformly as a single source of truth. Now, let’s have a look at why organizations need more data engineers and how they differ from Data Scientists.
Need for Data Engineers
Earlier Data Scientists were expected to develop fundamental data systems and data pipelines as a part of their job which led to data modeling processes being performed erroneously. Likewise, there would be a great deal of repetitive work and irregularity in the utilization of data among them. These issues hindered the organization’s ability to extract optimal value from their data projects and that’s when they fizzled. This prompted a high pace of Data Scientist turnover.
In the race of transforming digitally and becoming AI-driven, organizations’ need for efficient Data Engineers skyrocketed. The reason behind this is that now organizations need wide groups of Data Engineers that can exclusively zero in on data processing in a way that allows them to extract value from it. But, what strives organizations to imply data engineering? Let’s check out!
Data Overloads: The rising need for data engineering
As you know, data comes in all shapes and sizes. It can be both structured and unstructured, in organizational records, databases, and repositories.
However, the majority of this data goes unused by the business for a variety of reasons, such as new high volume sources, departmental tasks in the cloud (sales, payroll, HR, marketing), and changing requirements (new formats, analytics, data visualizations).
All these factors gave rise to the term- Data overload, also known as information glut, data smog, or information tsunami. In this VUCA world, enterprises are dealing with a huge problem of data overload. Their unorganized data has left most of their data unused and reduced decision-making capacity as well as restricted business driving. To overcome data overload issues, organizations are trying to build more efficient data systems and that is where Data Engineering comes in the limelight.
5 main causes of information overload today
- New working patterns
- Big data software development
- Changing customer expectations
- Compliance & transparency
- Multiple communication mediums
Till now we went through common business challenges, the basics of data engineering, and its rising need for leading organizations. I hope you are now aware of how data engineering can make a difference to your business by managing its overall data efficiently. Let’s now go through the five top-notch data engineering services that utilize cutting-edge & trending market technologies.
5 Top-Notch Data Engineering Services
Cloud data engineering
The first is cloud data engineering. This data engineering technique helps you to achieve a clear AI vision to build a robust strategy across cloud, technologies, and data governance pillars to modernize your data platforms. Additionally, you also gain:
Product Modernization with AI
The next is modernizing your data platforms by adopting AI and modern accelerators like Snowflake that can help you operationalize your product quickly with controlled costs.
Data process automation
Moving ahead with Data process automation. With this you can convert your existing processes into automated pipelines and customize them as per your business requirements with the help of multiple tools & technologies. With data process automation you can:
- Convert business processes to logical steps for code development
- Develop defined codes at each step for smooth integration into the pipeline
Serverless data processes
Now comes serverless data processes using cloud-based products. Serverless data processes allow you to tailor pipelines based on your business use cases and web services to be deployed. With serverless data processes you can:
- Select particular services and cloud platforms based on the client requirement
- Develop functions like AWS-Lambda, Azure, GCP, etc for each step inside the cloud services
- Integrate each step by creating logical event-based triggers to initiate processes as per your requirement
Last but not least is Data Visualization. With this you can create powerful visualizations and reports to generate valuable insights for your organization that will empower you to make your difficult business decisions and drive growth. With modern-age data visualization solutions that support a wide variety of formats and data structures, you can:
- Analyze relationships and patterns in various business activities and emerging trends
- Turn your organizational data into intuitive and informative charts and graphs
- Simplify the process of making data-driven decisions
- Improve communication of business insights to your employees and clients
How can your organization benefit from data engineering?
As we discussed the top data engineering services, I assume you are pretty clear about their utilization in your business. Now, allow me to take you through their overall benefits. Of course, the major & biggest benefit is that you can help your organization with highly efficient data processing. But, I would say that if you have a great team of data engineers that are well-equipped with the knowledge of leading-edge data engineering practices, you can build great data engineering solutions to achieve:
- Crystal-clear data segregation & governance
- Single store Data lake solutions
- Better data-handling
- Automated data processes
- AI-based data platforms
- Business-focused analytics
- Accelerated time-to-value
- Reduced cost-of-quality
At the end of my blog, all I would like to say is that adequate data engineering services can definitely help your company replace costly, oppressive in-house data infrastructure and transform huge data pipelines into strong systems to achieve effective business analytics.
Reach out to us at Nitor Infotech to see how we can ensure that your data stored in a perfect spot and correct format by developing new data architecture using a single source, or by incorporating new and existing data sources to make more viable & simpler data lakes.