Imagine a special system that’s really good at handling constantly changing information. This system, called an incremental data pipeline, helps process data as it’s created or changed. It’s like having a super-fast way to make sense of all the information that’s always evolving.
Why It Matters ?
The cool thing about this system is that it keeps your information up-to-date and accurate. This is super important for making smart business decisions. It also helps you react quickly when things change, like when new trends pop up or when something unusual happens.
How It Works ?
Setting up an incremental data pipeline involves using a bunch of smart tools and technologies. These tools help organize, transform, and process the data in real-time. It’s like having a team of experts making sure everything runs smoothly.
I will soon provide a detailed explanation of how it operates.
Adapting to a Changing World
In the fast-paced world of business, being adaptable is key. Lots of companies are now using modern technology to create something called a Data Lake by leveraging incremental data pipeline.
Datalake is special place in the cloud like a giant storage area for all kinds of information, from structured lists to messy notes.
Keeping Things Safe and Sound
This Data Lake is designed to keep everything safe, no matter what type of information it is. It uses special formats like Parquet, Avro, and Csv to make sure nothing gets messed up.
Challenges with data lakes
While data lakes offer many benefits, they also pose some challenges. These challenges include:
1 . CDC (Change Data Capture): It is difficult to implement CDC in data lakes.
2 . ACID compliance: Data lakes are typically not ACID-compliant, which means that there is a risk of data inconsistency.
3. Schema evolution: It can be difficult to handle schema evolution in data lakes.
4. Time travel: It can be difficult to implement time travel in data lakes.
5. Deletion of data: It can be difficult to delete data from data lakes.
6. Concurrent write: It can be difficult to support concurrent writes to data lakes.
But with the right solutions, these challenges can be overcome.
I will go through in details about how to overcome these problems in next article.
Thanks for reading .