Loan Application Analytics with CUFX

Credit unions maintain applications for different loan products in multiple sources and spend lot of engineering and reporting time to answer the business questions related to loan application. It becomes more challenging to have a unified view of all loan applications and perform further marketing and predictive analytics. CUFX (Credit Union for Financial Exchange) provides a standard schema model so that all business units can use the same nomenclature.

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Loan Application Analytics with CUFX

Text Normalization with Spark – Part 1

Numerous methods such as text mining, Natural Language Processing (NLP), information retrieval, and so on, exist for analyzing unstructured data. Due to the rapid growth of unstructured data in all kinds of businesses, scalable solutions have become the need of the hour. Apache Spark is equipped with out of the box algorithms for text analytics, and it also supports custom development of algorithms that are not available by default. In this blog post, our main goal is to perform basic text normalization using simple regular expression technique with Apache Spark and then decipher Spark stages, jobs, and DAG’s in the next blog post.

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Text Normalization with Spark – Part 1

Big Data Pipeline Architectures

Prior to jumping on the big data adventure, it is important to ensure that all essential architecture components required to analyze all aspects of the big data set are in place. Understanding the high level view of this reference architecture provides a good background of Big Data and how it complements existing analytics, BI, databases and systems. Treselle has solved interesting Big Data problems that required different types of architectures most apt for that particular business use case.

read more

Big Data Pipeline Architectures

The Five Layers of Microservice Testing

Microservice architectures enforce clearer, more pronounced internal boundaries between the components of an entire system than monolithic applications typically do. This can be used to the advantage of testing strategies applied to microservices; more options for places and methods to test are available. Because the entirety of the application is composed of services with clear boundaries, it becomes much more evident which layers can be tested in isolation.

read more

The Five Layers of Microservice Testing

Data Matching – Entity Identification, Resolution & Linkage

Data matching is the task of identifying, matching, and merging records that correspond to the same entities from several source systems. The entities under consideration most commonly refer to people, places, publications or citations, consumer products, or businesses. Besides data matching, the names most prominently used are record or data linkage, entity resolution, object identification, or field matching.

read more

Data Matching – Entity Identification, Resolution & Linkage

Loan Application Analytics with CUFX

Credit unions maintain applications for different loan products in multiple sources and spend lot of engineering and reporting time to answer the business questions related to loan application. It becomes more challenging to have a unified view of all loan applications and perform further marketing and predictive analytics. CUFX (Credit Union for Financial Exchange) provides a standard schema model so that all business units can use the same nomenclature.

read more

Loan Application Analytics with CUFX

Loan Application Analytics with CUFX

Loan Application Analytics with CUFX

Credit unions maintain applications for different loan products in multiple sources and spend lot of engineering and reporting time to answer the business questions related to loan application. It becomes more challenging to have a unified view of all loan applications and perform further marketing and predictive analytics. CUFX (Credit Union for Financial Exchange) provides a standard schema model so that all business units can use the same nomenclature.

Text Normalization with Spark – Part 1

Numerous methods such as text mining, Natural Language Processing (NLP), information retrieval, and so on, exist for analyzing unstructured data. Due to the rapid growth of unstructured data in all kinds of businesses, scalable solutions have become the need of the hour. Apache Spark is equipped with out of the box algorithms for text analytics, and it also supports custom development of algorithms that are not available by default. In this blog post, our main goal is to perform basic text normalization using simple regular expression technique with Apache Spark and then decipher Spark stages, jobs, and DAG’s in the next blog post.

read more

Text Normalization with Spark – Part 1

Text Normalization with Spark – Part 1

Text Normalization with Spark – Part 1

Numerous methods such as text mining, Natural Language Processing (NLP), information retrieval, and so on, exist for analyzing unstructured data. Due to the rapid growth of unstructured data in all kinds of businesses, scalable solutions have become the need of the hour. Apache Spark is equipped with out of the box algorithms for text analytics, and it also supports custom development of algorithms that are not available by default. In this blog post, our main goal is to perform basic text normalization using simple regular expression technique with Apache Spark and then decipher Spark stages, jobs, and DAG’s in the next blog post.

Big Data Pipeline Architectures

Prior to jumping on the big data adventure, it is important to ensure that all essential architecture components required to analyze all aspects of the big data set are in place. Understanding the high level view of this reference architecture provides a good background of Big Data and how it complements existing analytics, BI, databases and systems. Treselle has solved interesting Big Data problems that required different types of architectures most apt for that particular business use case.

read more

Big Data Pipeline Architectures

Big Data Pipeline Architectures

Big Data Pipeline Architectures

Prior to jumping on the big data adventure, it is important to ensure that all essential architecture components required to analyze all aspects of the big data set are in place. Understanding the high level view of this reference architecture provides a good background of Big Data and how it complements existing analytics, BI, databases and systems. Treselle has solved interesting Big Data problems that required different types of architectures most apt for that particular business use case.

The Five Layers of Microservice Testing

Microservice architectures enforce clearer, more pronounced internal boundaries between the components of an entire system than monolithic applications typically do. This can be used to the advantage of testing strategies applied to microservices; more options for places and methods to test are available. Because the entirety of the application is composed of services with clear boundaries, it becomes much more evident which layers can be tested in isolation.

read more

The Five Layers of Microservice Testing

The Five Layers of Microservice Testing

The Five Layers of Microservice Testing

Microservice architectures enforce clearer, more pronounced internal boundaries between the components of an entire system than monolithic applications typically do. This can be used to the advantage of testing strategies applied to microservices; more options for places and methods to test are available. Because the entirety of the application is composed of services with clear boundaries, it becomes much more evident which layers can be tested in isolation.

Data Matching – Entity Identification, Resolution & Linkage

Data matching is the task of identifying, matching, and merging records that correspond to the same entities from several source systems. The entities under consideration most commonly refer to people, places, publications or citations, consumer products, or businesses. Besides data matching, the names most prominently used are record or data linkage, entity resolution, object identification, or field matching.

read more

Data Matching – Entity Identification, Resolution & Linkage

Data Matching – Entity Identification, Resolution & Linkage

Data Matching – Entity Identification, Resolution & Linkage

Data matching is the task of identifying, matching, and merging records that correspond to the same entities from several source systems. The entities under consideration most commonly refer to people, places, publications or citations, consumer products, or businesses. Besides data matching, the names most prominently used are record or data linkage, entity resolution, object identification, or field matching.