What is Hadoop?: SQL Comparison

Summary

In this video, Jared Hillam explains three key differences between Hadoop and traditional SQL databases. He discusses the contrasting approaches of Schema on Write vs. Schema on Read, highlighting how data is structured and stored differently in both systems. Hadoop's flexibility and scalability are emphasized, especially in handling large datasets across many servers, where eventual consistency is preferred over strict consistency. The video also touches on how Hadoop’s complex nature can be simplified using tools like Hive, allowing for SQL-like queries without needing Java expertise. The importance of Hadoop in processing unstructured data is also noted.

Takeaways

• 😀 Schema on Write vs. Schema on Read: SQL requires predefined structure before writing data, while Hadoop applies the structure only when reading data.
• 😀 SQL databases use structured tables with defined columns, whereas Hadoop stores data in compressed files across multiple nodes.
• 😀 Hadoop replicates data across several nodes for fault tolerance and scalability, which is key to handling massive datasets.
• 😀 SQL databases use a 2-phase commit to ensure complete consistency across all nodes before releasing any data, suitable for transactional applications.
• 😀 Hadoop uses eventual consistency, providing answers even if some servers are temporarily unavailable, ideal for continuous data feeds.
• 😀 Hadoop's flexibility allows for creative processing programs, but requires knowledge of Java for custom queries, making it more complex to work with.
• 😀 Tools like Hive allow non-programmers to query Hadoop data using SQL-like syntax, reducing the barrier to entry.
• 😀 SQL is best suited for structured, transactional data, while Hadoop is designed for large-scale unstructured data processing.
• 😀 Hadoop’s architecture is built to scale across thousands of servers, enabling the processing of big data without being constrained by traditional infrastructure.
• 😀 Data management tools are increasingly making Hadoop accessible to businesses by simplifying the coding requirements and offering codeless access to data.
• 😀 Intricity offers solutions to help businesses integrate Hadoop into their existing infrastructure without the need for expensive data scientists.

Q & A

What is the key difference between 'Schema on Write' and 'Schema on Read'?

The key difference is that 'Schema on Write' requires predefined rules and data structure before writing data to the database, ensuring the data fits the expected format. In contrast, 'Schema on Read' allows data to be stored without predefined structure, and the structure is applied when the data is read, providing greater flexibility in handling unstructured data.

Why does Hadoop use a 'Schema on Read' approach instead of 'Schema on Write'?

Hadoop uses 'Schema on Read' to handle unstructured data more efficiently. This approach allows data to be stored in its raw form and the structure to be applied only when the data is accessed, offering flexibility to work with diverse data types without upfront constraints.

What does 'Schema on Write' imply in traditional SQL databases?

In traditional SQL databases, 'Schema on Write' means that data must adhere to a predefined structure before it is written to the database. This ensures that the data is consistent with the database schema and can be validated based on predefined data types and relationships.

How does Hadoop store data, and how is it replicated across nodes?

Hadoop stores data in compressed files (text or other types) in its Hadoop Distributed File System (HDFS). The data is then replicated across multiple nodes in the system for redundancy and fault tolerance, allowing for scalability and reliable data access.

What role does data replication play in Hadoop's scalability?

Data replication in Hadoop ensures that multiple copies of data are stored across different nodes, enhancing data availability and fault tolerance. This replication allows Hadoop to scale effectively, as it can distribute data processing across many nodes and still maintain data consistency even if some nodes fail.

How does Hadoop handle large-scale queries, like searching for specific words in massive datasets?

Hadoop distributes large-scale queries across multiple nodes in the cluster. A Java program defines the query, and the workload is split across the nodes processing parts of the data. After the processing is completed on each node, the results are consolidated to provide the final answer, improving efficiency in large datasets.

What happens if a server fails during a Hadoop query operation?

If a server fails during a Hadoop query operation, Hadoop will still provide an immediate response, focusing on eventual consistency. This means the query may not be fully complete, but the system delivers partial results promptly and will later synchronize to ensure consistency.

How does Hadoop's approach to consistency differ from traditional SQL databases?

Hadoop uses an 'eventual consistency' model, meaning it provides immediate partial results even if some nodes are unavailable, with the final result being updated later. In contrast, traditional SQL databases follow a '2-phase commit' approach, ensuring that all data across nodes is consistent before any results are returned.

Why is Hadoop considered flexible, and what challenges does this flexibility introduce?

Hadoop is flexible because it can scale across an unlimited number of servers and handle vast amounts of unstructured data. However, this flexibility comes with the challenge of increased complexity in managing and querying data, as users often need to write custom programs (such as Java-based MapReduce jobs) to interact with the system.

How have companies like Facebook addressed the complexity of working with Hadoop?

Facebook created Hive, a tool that allows users to write queries in SQL, abstracting away the need to write complex Java code for interacting with Hadoop. This helps users without programming expertise to easily query and manage large datasets in Hadoop.