Importance of Data
So we know In today's world, data is a key component to making money, especially in the tech world. Even in startups with a product they develop, the sales of this product and advertising costs are driven by data. Just to quantify how much data costs, here are some stats :
1] A single email address of an internet user is estimated to be worth around $89 to brands for targeted advertising purposes.
2]The value of user data exchanges among online service platforms is significant, with some major companies spending billions of dollars annually to acquire customer data from third parties.
3]Many major corporations also don't sell their consumer data because it is valuable, indicating that the revenue generated from such data can be substantial.
yaa, so data is money, and you need people who can manage this data. Data Architects are, in particular, required to give your total scenario a structure starting from where data comes to how you will store it and how you will use it. But architects just give you a structure; you need Data Engineers to implement the whole process.
So,
1] Data Engineering involves understanding what kind of data you deal with.
2] how will you connect to your source of data and fetch this data?
3] Initially, this data will have a lot of unwanted details, so how will you clean this data and make it usable?
4] once the data is clean, how will you store it further and make it available for end users or analytics teams?
what are all the technical terms Involved?
Data Pipelines: Like any other pipeline, data pipelines are created to fetch data from any source, enrich it and store it. These pipelines are divided into some components. Different tools like Airflow and Azure Data Factory can handle the orchestration. another component is code which cleans your data and re-structures it according to client needs. This is where Databricks comes into the picture. Usually, code is written in Scala or Python (Pyspark).
All clients will have different needs and according to that, you will have to create your pipelines.
Parallel Processing: Such a huge amount of data needs a huge processing power. Initially, Hadoop was used but now Spark is a must for a Data engineer.
Databases: Hive or Snowflake are usually used to store your data once enriched, as it is easier to make it available for other teams.
Going ahead, I'll cover more details on the overall process and technologies involved. This was just a brief on data engineering.