For businesses worldwide, data has become more crucial than ever. Companies are relying on it to gain useful insights into their business and achieve maximum operational efficiency. Whether you’re looking to improve the quality of your products or services, optimize resource utilization, boost marketing efforts, or avoid costly mistakes, data can help.
From product development to sales, companies are collecting data at all stages. And by data, we mean data that is either too large in size or very complex to be dealt with using traditional data processing applications. Unfortunately, a lot of organizations have still not unleashed the full potential of data analytics tools and techniques. In this blog, we are discussing data engineering and how it can help drive the right business decisions.
What is Data Engineering?
Data engineering means designing and building technological solutions for the efficient collection, storage, and analysis of data. It aims at converting a large amount of raw data collected from various sources to obtain meaningful information for strategic decision-making. Because the task is complex, it requires specialized knowledge and skills carried by professionals, also known as data engineers, who build and maintain infrastructure for data, usually collected in data repositories.
Also known as a data library or data archive, a data repository is basically a single, large database that contains a company’s overall data aggregated from various systems. The different types of data repositories include data lakes, data warehouses, data marts, and metadata repositories. Although often used interchangeably, these depositories serve different purposes.
Data lakes contain raw, unstructured data while data warehouses have processed data available for specific analysis. Data marts contain business data that is even more specific and focuses on a single functional area of an organization with predefined and computed performance metrics. Metadata repositories are only used to store metadata, i.e., the description of the structure of the data warehouse and objects inside the repositories.
Design and build data repositories to aggregate data across your systems into a single store and get a 360-degree view of your organization with data analytics. Know more about data engineering services.
An ETL (Extract, transform, load) pipeline is a set of tools and processes that are used to extract and move a large amount of data from various disparate sources to a centralized location, i.e., a data warehouse. Cloud data engineering experts develop ETL pipelines to perform various tasks such as sorting, joining, filtering, reformatting, merging, and converting unstructured data into structured data.
Traditional ETL pipelines developed by in-house teams only focus on moving data that was on-premises, however, modern-day ETL pipelines are capable of moving data stored on-premises as well as in the cloud. Most importantly, they are automated pipelines that use push or pull mechanisms to automatically fetch data through APIs or file transfers.
When it comes to building solutions for an ETL pipeline, there is a multitude of approaches that can be used to integrate data from different sources. Knowing what’s right for you can help you better meet your data engineering goals and obtain valuable insights for business growth.
Opt for data engineering consulting services to develop a robust ETL pipeline that can continuously stream-in clean and standardized data, readily available to be accessed by analysts and decision-makers.
Key Challenges Pertaining to Data Aggregation
- Security Vs. Convenience – Having a large amount of data consolidated in one place is a big relief as it makes it easy for the company’s stakeholders to access various datasets on the go. However, it can also have devastating consequences in case there is a security breach. Therefore, the protection of data with strict user access control and secured networks is critical.
- Standardization of Data Fields – Companies collect data through multiple systems, such as CRM, ERP, etc., which is why it is often fragmented and available in different types and formats (relational, logs, JSON, etc.). Establishing a standardized dictionary of data fields is vital to maintaining consistency and better analysis of data.
- Data Duplication – When you don’t have a proper data collection infrastructure in place, there is always a chance of data getting duplicated, which can lead to increased storage costs and false insights. Duplicate data must be removed or merged with existing data for correct and efficient analysis.
- Compliance – Data collected by companies is subject to various standards and regulations set by the government such as GDPR, CCPA, HIPPA, PCI, etc. Inconsistencies in data collection/aggregation can mean failing to meet these compliance requirements. With the right tools, a data engineer can provide built-in support for internal and external compliance requirements to avoid legal penalties.
Data makes no sense if it doesn’t serve a very important purpose—visualization. With a large amount of complex, unstructured, and inconsistent data, analysis is next to impossible primarily because of its sheer size. This is where data visualization comes into the picture. Data visualization helps you get a true picture of your organization by measuring the performance of your business against pre-set KPIs and making informed decisions.
Data visualization tools such as Power BI, Tableau, QlickSense, Dundas BI, Sisense, etc., can be used to create user-friendly and interactive dashboards, reports, charts, and graphs for easy understanding and quick analysis of data. Data visualization can help businesses identify existing and potential problems in the processes, understand market trends for business growth and explore opportunities to optimize operations.
Find the Right Data Engineering Solutions for Your Business Needs
Data, when managed effectively, can do wonders for your business. At Contata, we help organizations unlock the true value of their data collected from various sources across the systems and obtain critical business insights, providing them a clear edge over their competitors. We have a team of data engineers who carry a significant amount of experience building pipelines for cloud or on-premises deployments.
Depending on your requirement, Contata can design and develop the right data engineering solutions that best fit your needs. By leveraging modern technologies like Artificial Intelligence (AI), Power BI, predictive analysis, and Machine Learning (ML) models, etc., we can:
- Build data repositories (data warehouse/data mart/data lake) to rapidly consolidate data in a single place, enabling quicker analytics and data insights to support essential business functions.
- Provide expert advice on selecting the right cloud or on-premises ETL pipelines for your data
- Suggest data-driven BI solutions and offer supporting structure to boost business intelligence
- Create interactive dashboards and analytics reports for easy tracking and management of your key performance indicators (KPI)
- Design and manage data updates through automated triggers
Looking for something else? Get in touch with one of our experts today to know more about our data engineering and analytics services.