Data Engineering Services: Business Approach & Main Focused Industries

Importance of Data Engineering

In this competitive era, most of the large-scale organizations are using different types of operational management tools like ERP, CRM etc. to manage their internal processes. These different tools require different databases to store information which leads to scattered data that too in various format. In these situations, it becomes difficult for business to get the true picture of their internal process or to report the company’s performance to outside world.    

Going with the global usage of data and platforms like Artificial Intelligence and IoT there is a huge amount of raw data that gets generated. This large volume of data been generated eventually affects the cost of data computing, storage and assessment. 

Data engineering organizations comes into rescue in such situations. These organizations help their clients to monetize & maximize the value of data they possess. However, the major role here is for the clients to effectively explain the key business problems or requirements they have so that effective solution is provided to them. Data engineering & analytic expert work along with client enterprise data resources in order to capitalize the problem statement.

Strategies That to be Followed

  • Understanding Requirements

This is a key step in resolving any challenges or getting the right solution to any business requirements. This involves working very closely with clients to better understand business needs or key pain area they have. A better understand could trigger right solution and effective implementation. This session helps in a better understanding of the current landscape and understand the key areas of pain.

  • Providing Right Solution

There is great need to provide business-specific customized solutions. The approach is to get through our pre-practice sessions followed by discussion, opportunity and strategy explaining. This overall process of technical brainstorming results in data analysis, architecture design and implementation as needed. Experts transform the business challenges by providing a sustainable, advanced, and assured platform along to improve efficiency.

  • Data Quality & Security

Another key aspect in any data engineering project is the data quality. This ensures we have the right level of control on data. This could be achieved by having the right level of data validation along with having a robust centralized master data management governance process. To add to it, the data storage should be in secured environment to avoid any kind of breach we could have.

  • Data Constraints & Audits

The ETL process that gets setup needs to have a proper data constraint added so that the different ETL flow under different environment doesn’t end up with data getting corrupted or duplicates. Also, there is need to perform regular auditing on the business rule that’s gets implemented in ETL process time to time.

  • Delta Loads

With constant push of data, the need of having delta loads is must. This ensure quick turn around time for business to see close to real time data results.

  •  Validation

The result of the entire exercise is to meet client requirement and the way to effectively understand the completion is through rigorous and quality validation. This ensures the right requirement delivery to client as per initial agreement.

Key Offerings of Data Engineering Services

  • Development of end-to-end complete Data Pipelines
  • Ingestion of Data from various sources & getting it into the desired destination
  • Managed different file format conversions
  • Data Transformations
  • Data Cleansing
  • Data Quality
  • Data Integrity Maintenance
  • Data Models Development
  • ETL and/or ELT jobs performance
  • Enrichment of Data for downstream Analytical Purposes
  • Data Analytics
  • Performance Tuning

Data Engineer’s Workflow

ETL: Key steps in data engineering services is to extract, transform and load the data as per business requirements. Extracting raw data from different sources systems and applying various transformations in order to get raw data transformed into business KPI driven data. These business transformations include applying business rules to raw data, performing different calculations on called data transformation. Last and essential is loading data into warehouses having a proper schema architecture.

Data Warehouse: One of the most critical steps in data engineering services is data storage. Storing the current data and previous data at the same place for generating future data analysis reports. 

In BI, data warehouse can be broken down into two main database systems: Online Analytical Processing (OLAP), Online Transactional Processing (OLTP). OLTP system processes data transactions and the essence of most business applications to keep steady operations of transactions (ex. ATM), while OLAP system supports the analysis side of it, working with large amounts of data to find trends, crunch numbers and find the big picture.

Data Modelling: The process of creating a data model to store data in the database. Also, data modeling is a process that analyzes data requirements for supporting operations and future business projects as well. Data modeling is also required in the organizational process to define the structure and relationship between data elements. 

Using data modeling, we are visually describing the business and clarifying data requirements. 

BI Tools: Analyzing the data, standard choices are Looker, Mode, Tableau, Microsoft Power BI, Tableau Desktop, TIBCO Jaspersoft, Tableau Online, etc.

Data Engineering: Industries We Served 

Banking & Financial Industry: The Use of data engineering in the Banking and financial services sector has been increased nowadays. As these sectors need fast-moving and accurate result-driven technology as their daily volume of client’s data and financial data is enormous. Usage of traditional databases may result in data loss, manual error or may exceeds its capacity. 

The benefits of using data engineering take less time in giving statistical data; it makes the payment services more accessible and quicker, recovers client’s history in seconds., client’s data transaction from different places, etc. Data engineering technology identifies identification proof, which is useful for organizations to detect fraud cases also. 

An organization can use this technology to market any newly launched scheme to our targeted audience.  

Retail and E-commerce: As per the latest statistical figure, retail & E-commerce industry’s sale is minimum 3.53 trillion US dollars and it is expected to grow till 6.54 trillion US dollars by 2022. As this industry is highly growing, data engineering plays a highly compatible role in managing customer’s data and their payment history. Also, using the data industry, it is easy to launch and market new schemes for our potential customers. 

This process helps to improve merchandising experience and ultimately, it results in enhancing the brand value of n less cost. 

Health Care: Data engineering Services & experts help many health organizations to lead the way and grow further with data engineering. This advanced technology is responsible for diagnosing heart and respiratory diseases quickly. It made us possible with the help of a new and improved algorithm. Also, it is less time-consuming helps in better analyzing heart rate and breathing pattern on a human being. 

Due to a lack of proper diagnosis and their time-consuming diagnostic processes, as per the latest research in the USA, 600,00 people die every year due to sudden heart problems and lack of timely diagnosis.

But now onwards, this immediate diagnostic process will make effective treatment start earlier. 

Manufacturing: Data engineering experts help all small & big manufacturing industries based on their past data to make actionable decisions. Those decisions turned out to improve their product quality and quantity as data engineering is capable of giving extreme minute information to make the right choice for future activities. 

This process makes the stats more reliable. 


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