Data Engineering Introduction

Are you a Novice and curious or interested to know what really a Data Engineer does?

Then you are in the right place.😊

Data engineering entails building effective data architectures, for collecting, storing, processing and maintaining large-scale data systems. The pressing need for extracting insights from data, organizations need to define approaches to collect massive data and store it in a useful state.
For better performance, and results, Data Engineers use a combination of tools and platforms in their work environment to achieve this.

As a DE expert, you get to learn:

1.Python,Java,Scala programming2.Be Conversant with Linux Environment3.SQL(standard query language) AND NOSQL4.Bash and Shell Scripting5.Data Warehouses and Data Lakes6.API’s7.Distributed computing8.Data Structures and Algorithms9.ETL(Extract ,Transform and Load )and   ELT(Extract,Load and Transform)10.Business intelligence tools(BI) and Databases

One of the Data Engineering roles involves Data migration from databases to data warehouses. Querying, analyzing data operations are performed by a Data analyst, Business intelligence analyst or Data Scientist.

In DE there are two types of Data Engineering tools namely:

I.Low-code tools- this involves no coding i.e Tableau,AWS QuickSightII.Code tools- using programming languages eg.Python

A popular low code ETL tool is Talend used for data migration across databases. Other tools are Stitch, Xplenty, Pentaho and Alooma.

In the world of big data, data of different format, volume and size is generated and needs analysis. For this case, relational and non-relational databases are used. While there exist important cloud databases it is important to note that ordinary databases like MSSQL Server, MariaDB, Oracle SQL, Mysql are used in small and medium sized businesses.

However, multinationals would like to use distributed databases like Apache Ignite,Apache Cassandra,Apache HBase,Hadoop since they use data-intensive applications.
Apache Kafka and Apache spark are used for data streaming,data preprocessing respectively.
Cron jobs can be scheduled and query optimized through automation using the ETL tools. PySpark, Spark SQL make up a data engineering toolkit. A DE can create, query databases, clean data and configure pipeline schedules.

Essential Best practices of a Data Engineer

Acquiring data that answers business needs
Designing actionable data pipelines architectures
Developing algorithms for data transformation.
Collaboration with the management to understand business needs.
Creating data validation rules associated with data analysis and visualization tools.
Ensure compliance with data governance and security policies.

Any consultations reach us here --->
Chris Notes withNicholas
Respects:Neville Omwenga