Data Engineering

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Data Engineering

Our Spark/Databricks/Cloudera-certified engineers can build, deploy, and maintain your business Big Data infrastructure. We helped a transportation company generate insights from IoT devices using sensor data and advanced ML models.

What is Data Engineering?

Data engineering focuses on capture, storage, movement, security and processing of data. Data engineering is performed when building pipelines, applications, APIs and system to process and consume data. Data Engineers are able to work with a multitude of systems and technology within the same organization to bring coherence and accessibility.

Why Data Engineering?

Data engineering helps to source, transform and analyze data from disparate systems and makes data more function. Whether the data is relational or non-relational data engineers work to simplify it for the users to utilize the entire data together.

The goal of data engineers is to process data so that it can be used by consumers without having to deal with the underlying complexities. This saves time as well as provides a better view of all the data that is available to the organization in different formats and different systems.

Data engineering is used from the very beginning of every AI project. The AI project starts with the collection of data , followed by data pre-processing(cleaning,normalization,etc) and finally this data is used to build different models depending on the requirements of the project.

Implementation of Data Engineering

Data engineers build data pipelines with one or more sources and destinations. Data is transformed, validated, summarized and enriched within the pipeline.

Data engineering thinks about the end-to-end process as “data pipelines.” Each pipeline has one or more sources and one or more destinations. Within the pipeline, data may undergo several steps of transformation, validation, enrichment, summarization, or other steps. Data engineers create these pipelines with a variety of technologies such as:

ETL Tools
ETL Tools

These are categories of technologies that processes and moves data between systems. These tools take data from source and and “transform” the data so that it can be loaded into a destination system for analysis.

Spark & Hadoop
Spark & Hadoop

Spark & Hadoop are used for non relationals datasets that are usually spread over clusters of computers. Since they are designed specifically for the cloud and large amount of data they are the backbone of most cloud based applications.

SQL
SQL

Structured Query Language(SQL) is the standard language for querying relational database. It is used to perform SQL tasks within a relational database. Since it is very popular, many tools are compatible and is especially useful when the source and destination uses the same type of database.

HDFS & Amazon S3
HDFS & Amazon S3

HDFS and Amazon S3 are specialized file systems that can store an essentially unlimited amount of data. They are also inexpensive, which is important as processing generates large volumes of data.

Python
Python

Python is a general purpose programming language. It has become a popular tool for performing ETL tasks due to its ease of use and extensive libraries for accessing databases and storage technologies. Python can be used instead of ETL tools for ETL tasks. Many data engineers use Python instead of an ETL tool because it is more flexible and more powerful for these tasks.