Data Engineering

The goal of Data Engineering is to provide organized, standard data flow to enable data-driven models such as ML models, data analysis. The above-mentioned data flow can get through several organizations and teams. To achieve the data flow, we use the method called data pipeline. It is the system that has independent programs that make several operations on stored data.

Introduction

First of all, we are surrounded by data in day-to-day life. It shows us that software engineering wants an additional category to have data engineering, which is useful in many real-time platforms like data storage, transportation, etc.

Data Engineering is the field associated with analysis and tasks to get and store the data from other sources. Then, process those data and convert them into clean data used in further processes such as Data Visualisations, Business Analytics, Data Science solutions, etc.

Data Engineering converts Data Science more productive. If there is no such field, we have to spend more time preparing data analysis to solve complex business problems. So, Data Engineering requires a complete understanding of technologies, tools, faster execution of complex datasets with reliability.

Our development process

Understanding business needs

We get all the necessary information from the technical departments.

Analysis of data sources

It is essential to go through data sources to maximize the value of structured and unstructured data.

Building a Data Lake

A data lake is a data repository system that stores raw and processed structured and unstructured data files.

Designing Data Pipelines

These are the most critical activities in the data pipeline because they turn data into relevant information and generate unified data models.

Automation and deployment

The most important parts in data development consulting – DevOps team take care of the management and deployment of the pipeline.

Testing

Testing, measuring, and learning — are important at the last stage of the Data Engineering Process.

What do we do as Data Engineers

Data Flow

We have to get input data in the form of XML data, batches of videos updated every hour, weekly batches of labeled images, and so on. Data Engineers consume data, design a model that can take those data from several sources, convert and store them.

Data Normalization

Data Normalization involves tasks that make those data more convenient to customers. We store the normalized data in a relational database or data warehouse. Data normalization and modeling are part of the transform step of ETL(extract, transform, load) pipelines. Another way of transforming the method is data cleaning.

Data Cleaning

Data cleaning is the process of fixing or removing the incorrect, corrupted, incorrectly formated, duplicate, or incomplete data within the dataset. If we combine many datasets, there are many problems like duplicating, mislabel, incorrect outcomes, unreliable outputs.

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