Store, organize and process your data efficiently
In today’s age with numerous companies opting for digital transformation are producing unimaginable volumes of new types of data. To pursue their journey towards digitalization, they deployed costly enterprise data warehouses along with data marts to store, process, and analyse it. This certainly brought them some success, but, unfortunately, they failed to achieve distributed, scalable, and reliable IT infrastructure or expertise, primarily because of the costs associated with these types of enterprise data architectures which overshadowed any potential benefits. However, today, we have a far better option to tackle this issue of data management- Big Data Engineering.
Let me take you through the basics of Big 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 modelling 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. So, what drives organizations to deploy big data technologies in their process?
Short answer- Data overloads.
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 a VUCA world, enterprises are dealing with the massive obstacle of data overload. 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 Big Data Technology comes into play.
5 main causes of information overload today
- New working patterns
- Big data software development
- Changing customer expectations
- Compliance & transparency
- Multiple communication mediums
So far, we’ve seen common business challenges, the basics of big data technology, and its rising need for leading organizations. I’m sure you can see how data engineering can make a difference to your business- all by managing its overall data efficiently. Let’s now go through the five top-notch big data engineering services that utilize cutting-edge & trending market technologies.
5 Top-Notch Data Engineering Services
Cloud data engineering
The first on my list is cloud data engineering. This big data engineering technique helps you achieve a clear AI vision and build a robust strategy across cloud technologies and data governance pillars to modernize your data platforms. With it, you gain:
- Platform and product modernization capabilities
- End-to-end cloud operating model
- Data lakes & cloud data warehouse
- BI enablement & managed services (DevOps)
Product Modernization with AI
The next is product modernization. It entails modernizing your data platforms by adopting AI as well as modern accelerators like Snowflake with which you operationalize your product rapidly and within budget.
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 using multiple tools & technologies. With data process automation you can:
- Convert business processes to logical steps for code development
- Define 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 to fit your business use cases and web services. 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 in our journey to infusing big data technology in our business processes 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 data-driven decisions and drive growth. With modern-age data visualization solutions that support a wide variety of formats and data structures, you can:
- Analyse relationships and trends in various business activities
- Turn your organizational data into intuitive and informative charts
- 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?
At this juncture, we’ve discussed the different ways in which you could leverage big data technology to smoothen your business process. Now, allow me to take you through their overall benefits. Of course, the biggest benefit is that you can help your organization with highly efficient data processing. Additionally, with a great team of data engineers that are well-equipped with the knowledge of leading-edge data engineering practices, you can 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
In conclusion, I would like to say is that adequate data engineering services can aid your company in replacing costly, oppressive in-house data infrastructure and transforming huge data pipelines into strong systems to achieve effective business analytics.
Reach out to us at Nitor Infotech to see how we can help you build manage your data effectively and read our case study to see how we helped a leading retail chain leverage powerful data insights for seamless execution.