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
1. New
working patterns
2. Big
data software development
3. Changing
customer expectations
4. Compliance
& transparency
5. 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:
- Platform and product
modernization capabilities
- End-to-end cloud operating model
- Data lakes & cloud data warehouse
- BI enablement & managed services (DevOps)
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
Data visualization
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.
