Mohit Motwani

The importance of ensuring that data is accurate, reliable, and usable for its intended purpose. Data quality tools indeed play a crucial role in assessing, monitoring, and enhancing the quality of data through various processes such as profiling, validation, cleansing, deduplication, and enrichment. Data quality is mostly referred to as these 5 points. accuracy, completeness, reliability, relevance, and timeliness.

A data lake is a centralized repository that allows organizations to store vast amounts of raw and unstructured data at scale. Data lakes can hold Structured, semi-structured, and even unstructured data. The best part about Data Lake is that it doesn't mandate any schema on the data, which means the user can retrieve the data as a custom schema(schema-on-read). There are many data lake solutions available like ADL Gen 2, Snowflake, and Databricks.

A pipeline can be any program that consumes some data and produces some data. It can be as easy as doing some transformations, changing data types of input data, and outputting that data. Tools like Apache Airflow, Glue, and ADF are used to orchestrate and monitor the pipelines. Data pipelines include getting data from different sources, many transformational steps, and loading to warehouses, etc. A pipeline might include multiple pipelines within it.

While loading the data in a warehouse, I might not know the target use or you want to give the user of the warehouse the ability to choose their custom schema, for that ELT comes to the rescue. When we create modern data storage solutions like Data Lake and Data Lakehouse, where the schema is defined while reading the data (Schema-on-read) and we want to load the data as is to the warehouse, ELT is used.

Adding to the previous answers, The best way I have found myself learning ETL is to relate it with the kind of tools you can use for each of Extract, Transform, and Load. Although all these tasks can be performed with one tool only, the business demand also has a say in that. Tools like ADF and Databricks can help you create ETL pipelines. More open source and powerful tool Apache Airflow can be used to orchestrate the ETL pipelines and monitor the ETL pipelines.

Create your data models and seek feedback on dedicated data engineering platforms such as GitHub, Stack Overflow, or Reddit. Join mentorship programs tailored to data engineering, where you can connect with seasoned professionals who offer guidance and support. Utilize LinkedIn to expand your professional network and connect with data engineering experts. Engage with industry professionals by sharing your projects, participating in discussions, and reaching out for feedback.Attend data engineering meetups, conferences, and networking events to interact with industry experts face-to-face.