How Does Hadoop Help Outsourcing Back Office Services?
Are you planning for data migration to the Hadoop?
If you have plentiful data, it makes sense. Otherwise, it can prove a fatal mistake. The firm wherein millions of employees work, the size of data is always massive there. Likewise, there are several enterprises that look for the best outsourcing back office services.
For stockpiling a large volume of data, Hadoop emerges as an epic name. Yahoo (in 2006), AWS and IBM (in 2017) are a few prominent corporate giants that have harnessed its massive storage & file distribution system.
Why do corporate biggies hire Hadoop?
Except a big storage capacity, what’s so impressive in it that attracts hundreds of data outsourcing companies worldwide?
Advantages of Hadoop’s Distributive File System:
- If you look for a huge data storage that doesn’t burn a hole in your pocket, it’s a cost-effective data repository.
- You should not worry about the variable structure of your data. The data management service providers keepoutsourcing your data stored in whatever form it is. Be it is structured, semi-structured or unstructured they deploy Hadoop’s distributed file system.
- Hadoop file system processes irrespective size of data in different ways using a variety of ways, like Hive, HBase, Spark and a many more engines.
- You can put data files in a readable format, for example-JSON, XML, or a CSV file.
- You don’t need to optimize the file formats. It has the provision of OCR (Optimized Row Columnar), Avro and Parquet to process variable clusters of data.
- The optimized file formats store data in binary codes so that only machines can decode it.
- All the above said optimized files use on-the-wire model. Thereby, transmitting an extract of data or terabytes of data between nodes would become a walkover for Hadoop.
What is the difference amongst OCR, Parquet and Avro?
As said above, these three denote the optimized file formats. This optimization qualifies Hadoop to process data without any friction. But, these are different in nature. How? Let’s go through their differences:
| Difference indicators | OCR & Parquet | Avro |
| Data stacking | Column wise | Row-wise |
| Analytical format | Read-heavy analytical workloads | Write-heavy transactional workloads |
| Processing | Simple | Complex |
| Compressibility | Higher | Relatively lower |
| Schema | Better | Superior |
This table explicitly suggests that you can amass massive data, like IoT, in OCR and Parquet files. Let’s say, you gather data collected from sensors in your showroom. Obviously, the size of that data would be huge. Therefore, you can harness the attributes of OCR or Parquet formats. They segment data into columns. This is how you can read and derive sense from this communicable data structure. Mostly, the data management outsourcing companies utilize it. Indeed, it takes no effort to compress voluminous files. Consequently, the burden of back office work is shared easily with automated data storage.
On the other hand, if you’ve more number of attributes, Avro format proves a blockbuster idea. It processes those amassed data sets row-wise.
As far as schema architecture of data is concerned, you need a structure that speaks louder than words. It can be a hierarchy or a table. The OCR and Parquet formats provide satisfaction. But if you want an outstanding architecture of your data, Avro is unbeatable.
Tips for outsourcing data companies to use formats:
- OCR: It’s commonly adapted to the Hartonworks & Presto. It deals with enterprise data, warehouse optimization, cyber security and threat management, IoT and streaming analytics. It provides an exhaustive range of open source software designed to manage big data and its processing. It bridges the rift between the cloud and the datacenter.
- Parquet: It’s a model perfectly adapted to a Cloudera project called Impala. It is also used in Apache Drill that is an open source parallel processing SQL query engine. It is equated to Google F1 developed in 2012.
- Avro: It fits perfectly into the file format of the Apache Kafka cluster. One of the biggest big data miner-Druid uses it. Even, Yahoo picked it for the big data processing, according to Nexla.
The modern scenario recommends smart solution for deriving possible solution. The world of AI and machine learning is taking a giant leap to smart solutions. The data based outsourcing companies exploit the big data to derive feasible solutions. This is how they are explicitly emerging as a helping hand through the back office services. Sooner, artificial intelligence will come up with unimaginable solutions. And these solutions will spring from the streamlined data repositories of Hadoop.
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