Play by Play: Hadoop.AI.ML.

Hello Readers,?

Let’s talk around things how everything tech is interrelated and why we care for Hadoop Ecosystem which is Apache Foundation’s infamous open-source software what AI and ML are evolving tech to understand the buzz and used cases around it. This is going to be historical ride and also can be dazzling circus too as there is good and evil analytics too.

Let’s talk Hadoop, which is evolved as Hadoop Distributed database and Projects from companies like Yahoo (Media Analytics Warehouse, Beyond Hadoop), Google (Big Query, Google File System), Facebook (Scribe, Hive, Hadoop).

Let’s now think how this Hadoop Analytics ecosystem has outgrown and FAANG companies adopted open-source technologies and made their custom versions.?

import pydoop.hdfs as hdfs

# Open a file on HDFS
with hdfs.open('/media/us/colarado/dataset1') as f:
    # Read the file
    data = f.read()

print(data)        

In Yahoo’s case, the data warehousing solution is quite interesting! Here’s what we?know:

• Yahoo! uses a custom-built data warehouse called MAW (Media Analytics Warehouse). This solution isn’t based on a single, off-the-shelf product but rather leverages a combination of technologies.
• A core component is likely Hadoop: Similar to Facebook, Yahoo! might utilize Apache Hadoop for distributed storage and processing of their massive datasets. This allows them to handle the huge volume of data efficiently across numerous servers.
• Focus on Scalability: Yahoo! is known for its enormous data warehouse, once claimed to be the world’s largest. Hadoop’s horizontal scaling capabilities are perfect for such needs.
• Beyond Hadoop: While Hadoop plays a role, there’s likely more to the story. Yahoo! has a reputation for innovation and might use other open-source technologies or custom-developed solutions alongside Hadoop for specific functionalities within MAW.

Unfortunately, specific details about Yahoo’s data warehouse architecture are not publicly available. However, the use of a custom solution built around Hadoop seems to be the general consensus.

Hadoop plays a crucial role in supporting AI and Machine Learning (ML) models in several ways:

1. Handling Massive Datasets: Traditional data storage solutions struggle with the immense volume of data required for training complex AI and ML models. Hadoop’s distributed file system, HDFS, allows you to store and manage these massive datasets efficiently across clusters of commodity servers. This provides the raw material needed to train and refine your models.

2. Parallel Processing Power: Training AI and ML models can be computationally intensive. Hadoop’s MapReduce framework enables you to parallelize the processing tasks across multiple machines in a cluster. This significantly reduces training times compared to running them on a single machine.

3. Data Preprocessing and Feature Engineering: Before training, data often needs cleaning, transformation, and feature engineering. Tools like Pig and Hive within the Hadoop ecosystem can handle these tasks efficiently on large datasets. This ensures the quality and relevance of data fed to your models.

4. Scalability and Flexibility: As your data volume and processing needs grow, Hadoop can easily scale up by adding more nodes to the cluster. This flexibility allows your AI and ML workloads to adapt to changing requirements.

5. Integration with AI and ML Frameworks: Hadoop integrates well with popular AI and ML frameworks like TensorFlow, PyTorch, and scikit-learn. This allows you to leverage HDFS for data storage and utilize these frameworks for model development and training within the same ecosystem.

That being said with share your love and support. thanks for reading, keep being awesome.

Disclaimer: Made in Love with Gemini AI