Hadoop
Apache Hadoop is an entire ecosystem of Big Data tools and it has highly reliable, scalable, distributed processing of large data sets using simple programming models.
Manages data processing and storage
Highly reliable, scalable, distributed processing of large data sets using simple programming models.
Highly reliable, scalable,
The emergence of Hadoop has changed the data landscape. with Hadoop, you can gain new or improved business insights from structured, unstructured and semi-structured data sources. Large volumes of data can were stored historically or present in siloed departments can be gathered and analyzed in one place at an affordable price. It has highly reliable, scalable, distributed processing of large data sets using simple programming models.
- Manufacturing
- Financial
- Health Care
- Insurance
- Retail
Solutions we Deliver:
- Personalized Product Offering
- Fraud Detection and Security
- Compliance and Regulatory Reporting
- Customer Segmentation
- Risk Management
Hadoop Services:
- HDFS
- Map Reduce
- Hadoop Streaming
- Hive and Hue
- Pig
- Sqoop
- Oozie
- HBase
- FlumeNG
- Zookeeper
- Whirr
- Mahout
- Fuse
Apply data engineering and applied analytics to a defined use case.
Evaluate an existing Hadoop data-processing workload with a representative daily input and its downstream analytics consumers. Examine data quality, job dependencies, access controls and restart behavior before choosing support or modernization work.
Define the engagement scope.
Start with the decision or workload the data must support. Identify source systems, update frequency, data quality and access restrictions before selecting the processing pattern. Connect ingestion, transformation and downstream use through an explicit ownership model so missing or inconsistent information can be investigated.
Validate the operating result.
Validate data completeness, schema changes, late arrivals and repeat processing. Where machine learning is included, define the evaluation set and the business meaning of an incorrect result. Review access controls and sensitive fields throughout the flow, including the data used for development and troubleshooting.
Prepare for a focused working session.
Bring representative datasets, source specifications, existing pipelines and the intended use case. Agree a first deliverable with measurable quality and freshness requirements. The engagement can produce a data-flow design, transformation rules, validation checks and an operational handover tied to the selected platform and deployment.
Make the next step specific.
Bring your operating context, priorities and questions. We’ll help identify the relevant next step.
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