Data Engineering Courses

A data engineer conceives, builds and maintains the data infrastructure that holds your enterprise’s advanced analytics capacities together.

A data engineer is responsible for building and maintaining the data architecture of a data science project. Data Engineers are responsible for the creation and maintenance of analytics infrastructure that enables almost every other function in the data world. They are responsible for the development, construction, maintenance and testing of architectures, such as databases and large-scale processing systems. As part of this, Data Engineers are also responsible for the creation of data set processes used in modeling, mining, acquisition, and verification.

• Data engineers are software engineers who have the primary focus on dealing with data engineering tasks
• They work closely with System Administrators, Data Analysis, and Data Scientists to prepare the data and make it consumable in subsequent advanced data processing scenarios
• Most of these activities fall under the category of ETL (Extract, Transform and Load) processes Practitioners in this field deal with such aspects of data processing as processing efficiency, scalable computation, system interoperability, and security
• Data engineers have knowledge of the appropriate data storage systems (RDBMS or NoSQL types) used by organizations they work for and their external interfaces
• Typically, data engineers have to understand the database and data schemas as well as the APIs to access the stored data  Depending on the task at hand, internal standards, etc., they may be required to use a variety of programming languages, including Java, Scala, Python, C#, C, etc

It is important to know the distinction between these 2 roles.

While there is frequent collaboration between data scientists and data engineers, they’re different positions that prioritize different skill sets. Data scientists focus on advanced statistics and mathematical analysis of the data that’s generated and stored, all in the interest of identifying trends and solving business needs or industry questions. But they can’t do their job without a team of data engineers who have advanced programming skills (Java, Scala, Python) and an understanding of distributed systems and data pipelines.

Broadly speaking, a data scientist builds models using a combination of statistics, mathematics, machine learning and domain based knowledge. He/she has to code and build these models using the same tools/languages and framework that the organization supports.

A data engineer on the other hand has to build and maintain data structures and architectures for data ingestion, processing, and deployment for large-scale data-intensive applications. To build a pipeline for data collection and storage, to funnel the data to the data scientists, to put the model into production – these are just some of the tasks a data engineer has to perform.

Data scientists and data engineers need to work together for any large scale data science project to succeed,

Data Engineer:  A data engineer needs to have knowledge of database tools, languages like Python and Java, distributed systems like Hadoop, among other things. It’s a combination of tasks into one single role.

Data Architect: A data architect lays down the foundation for data management systems to ingest, integrate and maintain all the data sources. This role requires knowledge of tools like SQL, XML, Hive, Pig, Spark, etc.

Database Administrator: As the name suggests, a person working in this role requires extensive knowledge of databases. Responsibilities entail ensuring the databases are available to all the required users, is maintained properly and functions without any hiccups when new features are added.

• Introduction to Data Engineering (Related Course: Data Engineering Training for Managers)
• Basic Language Requirement: Python (Related Course: Introduction to Python Programming)
• Solid Knowledge of Operating Systems (Related Course: Unix Training)
• Heavy, In-Depth Database Knowledge – SQL and NoSQL (Related Course: NoSQL Training and Courseware)
• Data Warehousing – Hadoop, MapReduce, HIVE, PIG, Apache Spark, Kafka (Related Course: Big Data Training)
• Basic Machine Learning Familiarity (Related Course: AI and Machine Learning Training)

The data engineering field is expected to continue growing rapidly over the next several years, and there’s huge demand for data engineers across industries.

The global Big Data and data engineering services market is expected to grow at a CAGR of 31.3 percent by 2025.

Data  wrangling  (a.k.a.  munging)  is  the  process  of  organized  data transformation  from  one  data  format  (or  structure)  into  another

Normally,  it  is  part  of  a  data  processing  workflow  the  output  of  which  is consumed  downstream,  normally,  by  systems  with  focus  on  data analytics  /  machine  learning

Usually,  it  is  part  of  automated  ETL  (Extract,  Transform,  and  Load) activities

Specific  activities  include: Data  cleansing,  removing  data  outliers,  repairing  data  (by  plugging  in some  surrogate  data  in  place  of  the  missing  values),  data  enhancement /  augmenting,  aggregation,  sorting,  filtering,  and  normalizing

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