This article aims to explain the role of Data Lakes in modern data architecture for implementing business intelligence in an organization.
What is Business Intelligence?
Business Intelligence (BI) is a term used to define the usage of data of yesterday and today to make better decisions about tomorrow. We can understand this as the function which ensures that raw data is transformed into meaningful information.
Businesses can later use this information to gain insights and make better decisions. Consider what firms have been attempting to do for a long time and on a regular basis. We hear about investing in technology and solutions that are meant to use data and analytics to solve business challenges.
BI, however, is less about technology and tools and more about using data, technology, and tools to create business insights. In a nutshell, BI involves gathering functional business requirements and translating them into technical solutions.
Designing data models, doing ETL (Extract, Transform, and Load) to convert raw data from operational source systems to meaningful information, and transferring that information to an analytics/destination database are all examples of how it can be done.
Businesses can use it to visualize as a real-time automatic dashboard. It is done to make informed decisions based on past data rather than on a “Gut instinct.”
What is the ultimate output of BI in Data Architecture?
BI provides a single version of the truth – It gives users a real-time, automatically updated, and consistent report.
BI provides descriptive analytics – It tells you what’s happening now and what happened in the past.
BI provides diagnostic analytics – It is about giving in-depth insights and answering the question: Why did something happen?
Data Lakes in the Modern Data Architecture
The word “business intelligence” is often used interchangeably with “data warehousing.” If business intelligence is the front end, data warehousing is the backend. As a result, we might think of it as the foundation for achieving business intelligence.
The ultimate goal of BI implementation is to transform operational data into useful information. As a result, raw data from several operational databases developed and optimised for running programmes rather than analysis can be scattered.
Sometimes to get one data field, you would have to do ten joins! And here it goes. People come up with a solution called central data storage — a data warehouse. Data warehousing solutions emerged in the 1980s. Businesses can optimize it for providing information or insights.
A data warehouse is a destination database that consolidates data from an organization’s source systems. It’s a relational database because, as shown in the physical data model, we may link data from multiple tables using the joint field.
The database structure establishes relationships between tables. As mentioned, the data sources connect into the data warehouse through an ETL process, known as Extract, Transform, and Load. Thus, a data warehouse follows the schema-on-write pattern, where the design fits the answers to the common questions.
Data Lake Architecture
Because data that goes into data warehouses must go through a rigorous governance process before being stored, adding new data items to a data warehouse necessitates altering the design, implementation, or restructuring of structured storage and the related ETL to load the data.
With such a large volume of data, this operation could take a long time and require a lot of resources. So the data lake notion emerges as a game-changer in large-scale data management.
The data lake notion first appeared in 2010s, and it is the idea that all of an organization’s structured, unstructured, and semi-structured data can and should be stored in the same location.
Apache Hadoop is an example of data infrastructure that enables the Data Lake architecture by allowing the storing and processing of vast amounts of data, both structured and unstructured. The read technique has a schema in the data lake.
It stores raw data. We can set it up so that the data structure and schema are not defined in the first place. To put it another way, when we move data to a data lake, we do so without any gatekeeping policies in place.
We apply the rule to the code that reads the data rather than configuring the data structure ahead of time when we need to read the data. In the data lake universe, instead of the traditional Extract, Transform, and Load procedure used in data warehousing, the method is Extract, Load, and Transform.
As a result, firms can use Data Lake to save money and conduct research. As a result, a Data Lake design allows enterprises to get insights from both processed and controlled data as well as raw data that was previously unavailable for analysis.
Raw data exploration can then potentially lead to business questions. The most serious risk with data lakes in modern data architecture is that, without proper governance, they can quickly become unmanageable data swamps. If business users don’t trust the data lake’s data quality, they won’t be able to access it.
Increasing Use of Data Lakes in Modern Data Architecture
Companies who seek to benefit from a data lake architecture more conservatively have recently emerged as a trend. Instead of establishing a more regulated data lake, these organisations are moving away from the ungoverned “free-entry” strategy.
The data lake can contain two environments: an exploration/development and a production environment. Data will be explored, cleansed, transformed to build machine learning models, build functions, and other analytics purposes.
The data lake’s production section stores data generated by the transformation process, such as metrics functions. Another trend is to not dump all of the raw data into the lake. Only’verified’ data is allowed into the regulated data lake.
Essentially, a governed data lake architecture does not restrict the data types stored in it, meaning that controlled data lakes still comprise multiple data types, including unstructured and semi-structured data like XML, JSON, CSV.
However, the key is to ensure that no data is stored in the lake. It needs to be described and documented in the business glossary before storing. It will give some confidence to the users about the quality and meaning of data.
There needs to be a governance process around this — which is all about roles and responsibilities, for instance, who owns the data, who defines it, who will be responsible for any data quality issues.
Going for this approach will be time-consuming. It is because prescribing data can be a long process since it involves people from different disciplines across an enterprise.
Can a Data Lake replace a Data warehouse for BI?
Without a question, Data Lakes in Modern Data Architecture provide significant advantages over traditional data warehouses, notably in terms of dealing with large amounts of data and cost-effectiveness. Business consumers, on the other hand, cannot trust data lakes unless they know about the data quality.
Many data scientists are unaware that 80 percent of the effort is spent acquiring raw data from numerous sources to be clean and high quality for modelling. Meanwhile, Data Warehouses’ design and discipline, which deliver controlled and high-quality data, must not be overlooked.
There is still a permanent and robust demand for a core set of KPIs that define how a business is doing, which are also needed for reporting, mainly regulatory reporting purposes. Such information demands highly governed information.
The most important question regarding what firms seek to achieve with data and technology in their business strategy must be answered unequivocally. Companies can control all of their data in a business glossary platform and parachute it into a Data Lake, but I doubt that will add value if they don’t know why.
I hope this article will help you in understanding the concept of data lakes in modern data architecture. If you want to get the data warehouse and data lake benefits, then Perimattic is the best place for you.