Technology is like the ocean, and it entirely relies upon you how much resources you can use
from it. If you are not obsessed or attentive enough to use resources from it, the ocean
will follow its natural course and others will take its advantage.
Putting this context in a concise manner, if your business is not going with the flow of
technology or not flexible enough to be mold as per the wave of innovation, then you could
be left behind forever in this competitive marketplace.
Giving a more meaningful narrative to this context, the technology we are going to discuss
is data, or more specifically data lake. Actually, the data lake is a kind of data reservoir
of large quantities of data in the native form to satiate your business requirement with
every imaginable insight. Data lake is all about data management, and a highly recommendable
tool to driving business value and enhance the decision-making ability of businesses in this
data-driven culture.
So to capitalize on these unprecedented business opportunities, you need to have a scalable
data management for your organization, flexible enough to support emerging data needs. But
the question is— from where you can get that scalable data management, which can satiate
your data needs and provide you easy access to all data sets without any loss of information
along the way? That’s why the concept of data lake has been emerged to give you a respite
from the data management concern.
It is an emerging concept equipped to unleash the business significance of relevant data for
detailed analysis. It works both ways with equal efficiency, within and outside an
organization. But from whence its need arises? And why it has replaced all the traditional
data warehouses and emerged as the modern data warehouse.
It is because Data Lake allows businesses to leverage more data types and enable faster
turnaround time, even with the large volume of data, which was out-of-bounds with a
traditional data warehouse. Furthermore, it delivers this optimal performance at reasonable
costs and creating new possibilities for traditional businesses for process reinvention. Due
to the compatibility with multiple data sources, such as—Web, Mobile, and other connected
devices along with text, graphs have further paved the way for the augmentation of data
management and the need for Data Lake.
Specifically, talking in terms of cost, then the overall cost in implementation and
operating a data lake is ten times lower than using the SQL-based traditional data
warehouse. So from a business perspective, especially when it comes to scalability and
efficiency in data management, Data Lake can create substantial business value and have
become quintessential in this digital business landscape.
Giants like Amazon, Netflix, Capital One, and others are already thriving on the power of
data by using data lake infrastructure. No industry is left behind to feel the influence of
this phenomenon—Energy, Healthcare, Finance, Mining, Education, Telecom, and others are
using the data analytics feature in different forms.
Even startups like Arundo Analytics, Civis Analytics, Formation & Gemini Data are making
data a more valuable asset for businesses and making a big voice in this industry.
Hereby mentioning some business values which
organizations have already begun to reap from data lakes—
Data Lake acts as an enabler for faster big data analytics—Data lakes are
customized for fast big data analytics, indeed, for real-time analytics. It can leverage
exploding quantities of data with certain algorithms to drive analytics.
Store and mix— structured unstructured in one Data Lake—Comes with the
unique ability to acquire, mix, integrate multiple data types, irrespective of their format
and source.
Data lakes—comes with the flexibility to scale your growing data—Presently,
data lakes are highly scalable and flexible. The infrastructure is automated to deal with
the exploding volume of data.
Saving enterprise data warehouse resources—Data Lake can act as the staging
area for the enterprise data warehouse, in which it only passes the query-based relevant
data to the warehouse.
Stringent Data security features—The security features with Data Lakes enable the organization to provide limited access to information. It also includes access to the original source content.
Centralized system to store Data—It is made in a suitable manner that it
stores data with all the attributes. It uses a flat architecture to store data instead of
store it in files and folders.
Faster insights every time—Since the data is assigned with a unique
identifier and tagged metadata, it empowers to extract and process data at a higher
speed.
Easy integration with the Internet of Things (IoT)—Multiple data such as
IoT device logs and telemetry can be collected and analyzed. We have examined this whole
process when we developed a prototype fitness app, Fitplus, and gathered all the fitness
data and sync it with the different fitness machines to control with a smart app. Click now
the entire model of functioning (https://bit.ly/33Rxeqd).
Seamless integration with Machine Learning (ML)—Data lake has a schemaless
structure and ability to a large amount of data. So it is perfect when we integrate machine
learning algorithms to large data. Like we did in Videobomb to create seamless Augmented Reality (AR) experiences that
enable a user to scan the popular tied up products and plays with their latest videos and
objects.
Flexibility—Its schemaless structure support analysis supports the analysis
of data from social networks and mobile devices. Going one step ahead, it supports large
heterogeneous, multiregional, and microservices environments
Agility—Data lakes are good for analyzing data from different, diverse
sources, and yes it can easily decode complex patterns.
Cloud Offerings—All the prominent & trusted players in cloud
technology, including AWS, Google, Cloud Platforms, and Azure, offer managed data lake
solutions. As it ensures speedy information and empowering businesses with marketing
insights from multiple data sets. Major cloud providers tend to offer data lakes rather than
data warehouses, given data lakes integrate better with organizations’ systems and are
better optimized for cloud environments.
Conclusion
In the current scenario, the data lake has delivered all its promises on scalable and efficient data management. It ensures speedy information and giving specific business insights from multiple data sets through data science and machine learning. From a data management point of view, there is immense potential in industrial applications of Data Lake, and the benefits we have mentioned above are just the beginning.
