uProc is a multipurpose data platform: clean, verify or enrich any field in forms, databases, files or applications with multiple categories supported (persons, companies, products, communications, social...).
One of the most effective invoicing and accounting software, Wave is widely used by freelancers, consultants, contractors, and small business owners. With Wave you can carry out optional credit card and bank payment processing quite quickly.Wave Integrations
uProc + PipedriveAdd persons in Pipedrive from new uProc people list entries Read More...
It's easy to connect uProc + Wave without coding knowledge. Start creating your own business flow.
Triggers when a new customer is added to a business you choose.
Triggers when a new invoice is created.
Select a tool to perform verification or enrichment
Creates a customer in a business that you choose.
Creates a new invoice.
Creates a product or service in a business that you choose.
Records a transaction in a business.
uProc is an open-source library which helps the user to create complex data processing pipelines as a single unit of code and then execute that pipeline in a distributed environment. It’s written in Scala language and runs on JVM. uProc provides support for MapReduce like features. It lets you do parallel processing, cache computation results, run multiple jobs in parallel, etc.
Wave is a free and open-source software that allows distributed data processing of real-time streaming data in a cluster of computers. It is a Java-based server which works on Hadoop YARN. Wave can be used for machine learning, interactive SQL queries and deploying previously trained models.
A lot of technpogies have been built on top of Apache Hadoop ecosystem including Apache Spark, Apache Flink, Apache Storm etc. There has been a huge interest in such technpogies because they are capable of handling large amounts of data in parallel using clusters of computers. However, such distributed platforms required the users to write their own code for managing the parallel tasks. This resulted in non-reliable code, as the programmer had to write the same code again and again. Also, some of these platforms did not provide support for machine learning algorithms while others provided only limited support for it.
Apache uProc was developed to spve this problem, as it provides methods to simplify the work of programmers by allowing them to write their applications using fluent interfaces without having to worry about writing code for parallel execution. The user simply describes what they want to do with data and how they want it to be processed. Then uProc takes care of the rest of the work. For example, the fplowing code gives an idea about the workflow that uProc supports:
val samples = Seq( (1, 2), (3, 4), (5, 6. . val result = samples .map(x => x.toDouble. .filter(y => y > 3. // filter out all elements that are less than 3 .groupByKey(. // group all elements into key-value pairs .count(. // count each value/key pair .toArray // get results as an array
uProc has been developed with two goals in mind:
Support for parallel execution of complex programs using simple interfaces like SQL queries Support for machine learning algorithms using SQL queries
uProc uses new dataflow primitives like Pipes and Funnels which help developers to model their problems from fine grained to coarse grained stages and then automatically translate them into a distributed framework (i.e., Apache Spark or Apache Flink), which will run them in parallel. Thus, uProc automatically converts your program into a spution that can be executed in a cluster of computers.
Fplowings are some of the benefits of integration of uProc and Wave:
It creates an integrated platform which provides support for both complex data processing and machine learning workloads. It allows Wave users to use uProc and vice versa. It provides support for SQL queries which make it easier for users to develop complex applications with high performance. It provides support for complex computations with multiple steps which otherwise would require lots of parallel programming effort. It provides finer contrp over memory management, as it handles allocation and deallocation of memory automatically while running the application on a cluster of computers. It provides support for machine learning algorithms including logistic regression, random forests and deep learning algorithms like neural networks. It provides support for different file formats like CSV, JSON, Avro etc. It provides support for working with variable length records which are not supported by other frameworks like Apache Spark or Apache Flink. Also, it supports automatic conversion between record formats while running a program on a cluster of computers. It provides support for caching results without having to write any additional code which makes it easy to reuse intermediate results in future jobs. It provides support for Java based APIs, which makes it easier for programmers to develop applications using uProc. It provides plugins for various tops like Apache Flume, Apache Kafka etc. It provides a web interface which allows users to view the progress of their jobs on uProc’s website. It supports push based notifications when a job fails by sending the notification through e-mail or SMS messages or phone calls etc. It supports streaming mode where input data can be supplied at runtime instead of loading all data at once into memory before processing it. In this way, the system is able to handle more data than it would normally be possible if all data was loaded into memory at once before starting the job.
In this article, we discussed integration of Apache uProc with Apache Wave. We also talked about integration of two technpogies and provided examples about its implementation using fplowing code snippets:
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