Big Data Parser
Increase productivity with pre-built parsers on Hadoop for industry standards and complex file formats.
Streamline development
Informatica Big Data Parser's example-based transformation dramatically increases productivity. Users view a data sample in original and text formats, which allows continuous development of the parser or data handler and provides instant feedback without the need to compile and deploy.
Accelerate deployment
Informatica Big Data Parser increases development speed on Hadoop up to five times faster than hand coding by supplying pre-built parsers for many industry standards (for example, FIX, SWIFT, NACHA, SEPA, ACORD, HIPAA, HL7, EDI, ASN.1). The Big Data Parser engine is accessible for Hadoop developers in a simple call, enabling the parsing of any data format inside Hadoop.
Rapidly deliver data
Informatica Big Data Parser provides a visual development environment to parse and transform structured and semi-structured formats rapidly into a usable, canonical, and flattened format. With Big Data Parser, Hadoop developers can use a single transformation engine, instead of multiple coded data handlers, to develop a single program agnostic to the data variation.
Key Features
Parse multi-structured and industry-standard data on Hadoop, including industry standards,documents, log files, and complex file formats.
Access and parse complex data
Parse a variety of industry standards, documents, log files, and complex file formats in Hadoop
Visual development environment
Use pre-built parsers or create customer parsers with Big Data Parser Studio, then test parsing transformations on sample data
Supports multiple industry standards
Supports multiple industry standards (for example FIX, SWIFT, NACHA, SEPA, ACORD, HIPAA, HL7, EDI, ASN.1)
Binary document extraction on Hadoop
Extract data from binary documents such as Microsoft Office and Adobe PDF on Hadoop
Process hierarchical structures on Hadoop
Process deep hierarchical data structures such as XML and JSON formats on Hadoop
Parse log files on Hadoop
Parse and extract data from a variety of log files (web, call-detail records, mainframe, and proprietary) on Hadoop
Elastic scaling
Process parsing transformations regardless of data format or size that scale with the topology of the Hadoop cluster
Customer Success Stories
M.D. Anderson Cancer Center
Informatica empowered scientific and clinical collaboration at this renowned cancer center by turning data into knowledge and facilitating self-service business intelligence.
