Data Engineering

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Overview

Data has become one of the most important assets for organizations to unearth insights and automate business processes. To implement automation with intelligence practices, companies need a wide range of professionals with skillsets like data analysis, data science, and data engineering.

Since data can come from various sources and are of different types, companies shall ensure they have the right expertise to deploy the latest technologies for business growth.

The Need For Data Engineering

Data Engineering Capabilities

Data Analyst

The job of a data analyst is to analyze data for a company, answer some crucial questions, and use the results to help make important business decisions.

Depending on various kinds of industry, the title of these roles can change, not always implying that their job will be very different.
For example, a data analyst can also be called a Business Intelligence Analyst , Operations Analyst, or Database Analyst .

The most common skill sets required to be a data analyst are:

Cleaning, organizing, and manipulating raw, unstructured data.
Using statistical tools and methods to get a meaningful view of their data.
Analyzing useful conventional and unconventional trends in the data.
Creating graphical visualizations and dashboards to help the company easily interpret and make decisions with clean data
.
Presenting the results of technical analysis to business heads, clients, or internal analysis teams

Data Scientist

A data scientist is a specialist who uses advanced statistical methods and algorithms to build machine learning models. Those models are used to predict the outcomes of complex scientific or business scenarios.

A data scientist also needs to clean, analyze, and visualize the data before using them to make advanced machine learning models that use complex mathematical models to produce some outputs according to given inputs.A data scientist has to work with both supervised models. These are mainly used to find or predict patterns inside some specific data.

Here is a list of popular data tools and technologies :

Data scientists require to do some of the following tasks:

Evaluating statistical machine learning models to determine the accuracy of the analysis.
Using machine learning algorithms to train predictive models.
Testing, improving, and maintaining the accuracy of machine learning models.
Building advanced analysis and summarizing the conclusions with data visualizations.

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Data Engineer

Data engineers build the systems upon which data scientists and analysts perform their work. They are also responsible for optimizing and improving those systems according to the complexity of the research/analysis work initiatives . Data engineers make sure that the data with which others are working is accurate and accessible to everyone.

They must ensure that the collected data is properly received, stored, and transformed effectively. A data engineer builds a software pipeline that can handle complex tools and techniques for data manipulation. Thus, creating, maintaining , and managing information with data pipelines are some of the job descriptions for a data engineer.
The data engineer’s mindset is focused on building and optimizing the workflow for analysis.

The following are examples of the tasks that a data engineer has to work on:

Making APIs for the consumption of data.
Integrating external or new datasets into existing data pipelines.
Applying feature transformations and feature extractions for machine learning models on new data.
Continuously monitoring, maintaining, and testing the system to ensure optimizations in performance.

There is a scarcity of data engineers and data scientists around the world. Since the amount of data is increasing exponentially every day, acquiring the skill sets to understand and manage that data takes time and patience. Numerous firms provide easy data solutions to businesses in a centralized way. One of them is RecoSense.

RecoSense Engineering Solutions Enable Organizations To

Gather data requirements such as the period for which it needs to be stored, how it will be used, and the probable editors of the dataset.

The more data one has, the more will be the efficiency of the model.

Maintain metadata about the same which contains the different technological boundaries and aspects of the data, namely the different relationships among the schemas, the actual size of the data, and the authority that will handle the data. It is very significant to structure the data carefully, bringing out the distinct features and patterns in the dataset.

Ensure security and governance for the data using centralized security protocols like LDAP, industry-standard data encryption, and monitoring access to the data. This amount of data should not and must not be misused.

Store it using specialized technologies such as a relational database, a NoSQL database, Firebase, Hadoop, Amazon S3, or Azure blob storage.

Data servers and storage solutions help keep data efficiently in a secure manner.

Process it with the help of tools that are used to access data from various sources, manipulate the data and bring out important features within the data and store them efficiently.

Different research servers, data centers are available to process numerous petabytes of data.

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