Hadoop vs MarkLogic: What are the differences?

1. Data Processing Approach: Hadoop is a distributed processing framework that works on the principle of splitting data into smaller chunks and processing them in parallel across multiple nodes in a cluster. In contrast, MarkLogic is a NoSQL database platform that stores and processes structured and unstructured data natively, allowing for real-time querying and analysis without the need for preprocessing or splitting data.

2. Data Storage Model: Hadoop relies on distributed file systems (such as HDFS) to store data in a distributed manner, which is optimized for high-throughput sequential I/O operations. MarkLogic, on the other hand, utilizes its own indexing and storage mechanisms that are designed to provide efficient retrieval and indexing of complex and varied data types, including JSON, XML, RDF, and binary files.

3. Query Language and Capabilities: Hadoop primarily utilizes MapReduce programming model for processing and querying data, which involves writing complex code for tasks such as filtering, sorting, and aggregation. In contrast, MarkLogic offers a powerful structured query language (XQuery) that allows for querying and manipulating data directly within the database, enabling complex searches, joins, and transformations without the need for external processing.

4. Schema Flexibility and Evolution: Hadoop is schema-on-read, meaning that data can be ingested without predefined structure, but the interpretation of the data's schema occurs at the time of processing, making it more flexible for unstructured or semi-structured data. MarkLogic, as a NoSQL database, supports schema-on-write, where data schema is enforced during ingestion, allowing for greater data validation and consistency over time.

5. Data Consistency and ACID Compliance: Hadoop is eventually consistent, ensuring data consistency across nodes over time, which may lead to potential issues with ACID transactions and real-time data consistency in certain use cases. MarkLogic, as a transactional database, provides strong ACID compliance, guaranteeing consistency, isolation, durability, and atomicity for all operations, ensuring data integrity and reliability.

6. Scalability and Performance: Hadoop's scalability is highly dependent on the size of the cluster and the distribution of data across nodes, making it suitable for processing large-scale batch workloads in parallel. MarkLogic's architecture is designed for horizontal scalability and can efficiently handle real-time querying and transactional loads with predictable performance, making it a preferred choice for mission-critical applications requiring low-latency access to data.

In Summary, when comparing Hadoop and MarkLogic, key differences lie in their data processing approaches, storage models, query languages, schema handling, data consistency mechanisms, and scalability and performance characteristics.