Advanced Analytics using Apache Hadoop HDFS System!

Hadoop does advanced analytics incredibly well with its efficient HDFS separating data from the state in its node architecture, using one over-arching node that manages state for the entire cluster, and several daughter nodes that store only data. However, the Hadoop cluster data nodes execute commands from their master node and log all operations in a static file which triggers a replica master to quickly recreate the entire state of the system without any needing to communicate with another master node in times of a fallback. This makes the system extremely fault tolerant, and prevents the split-brain scenario that causes data loss amongst masters that must communicate with each other to restore state.

Apache Hadoop also has a wide ecosystem of tools which is comprised of support bulk uploading and ingestion of data, integrated with SQL engines. This helps to retain support for the full querying power expected from a standard database software. However, it can be subjected to the conflict that standing up Hadoop, Zookeeper, and a Kafka ingestion agent requires as much domain specific knowledge as Hadoop Ecosystem. Thus, the raw power and stability of Hadoop come at the price of heavy setup and maintenance costs.

Hadoop’s powerful MapReduce query framework is robust enough to handle any data aggregation or transformation job. But mastering the intricacies of MapReduce is a high overhead for the simple operations needed in most web analytics tasks. This means analytics systems built on Hadoop will also need to deploy a query engine layer, driven either by HiveQL or Facebook’s real-time Presto engine, so analysts can interact with the dataset using familiar SQL instead of MapReduce. These engines are incredibly powerful, but add an additional layer of complexity to the analytics infrastructure.

Hadoop remains the reigning champion in the analytics world which leaves the world with no choice but to learn and adapt the Hadoop Advanced Analytics. While Hadoop Ecosystem is a great tool for simple web analytics, it's unforgivable sin of streaming data loss during ingestion, and arduous data ETL process make it untenable as the foundation of a complete analytics system. It’s a great tool for toy analytics and plug-and-play visualization, but its production scalability issues mean this technology still isn’t ready for prime time.

Implementing a Hadoop instance as the backbone of an analytics system has a steep learning curve, but it’s well worth your effort. In the end, the system will enjoy increased stability with rock solid ingestion and broad compatibility with a number of third-party analytics tools, including Hadoop Ecosystem via the Hadoop Ecosystem-Hadoop connector. On the other hand, if you’d like to avoid having to stand-up a full-fledged Hadoop cluster on your own, check out Treasure Data. We’re a cloud-based solution that can integrate with your web or mobile app in just a few lines of code and start capturing data instantly. We let you store and query the raw data in a schema-on-read data lake, and provide tools to run transformation or machine learning workflows that output to many third party destinations, including Hadoop Ecosystem.