The document describes a training course on CloverETL, an open-source data integration tool. The course goals are to learn how to compose transformations to connect to various data sources and sinks, handle errors, and design complex job flows. The course modules cover basics of the tool, common components, databases, structured data, advanced graph design, and the CloverETL Server. It also provides information on the CloverETL product family and describes how to work with metadata and use the Reformat transformation component to write code directly in CTL.
Presenting Presenting changes in embedded CloverETL for IBM Infosphere MDM Master Data Management and new licensing options for CloverETL Designer and Server for partners and end users.
Excerpt from the CloverETL Basic Training slides.
The basic course lasts 3 days and covers basic principles, CloverETL Designer walkthrough, transaction analysis, lookups, database connections, working with structured data, XML etc.
More at www.cloveretl.com/services/training
Tony Scalese, Edgewater Ranzal Oracle Financial Data Management (FDM) practice director, presented "Getting the Most Out of FDMEE in a Multiproduct Environment" at KScope14.
Presenting Presenting changes in embedded CloverETL for IBM Infosphere MDM Master Data Management and new licensing options for CloverETL Designer and Server for partners and end users.
Excerpt from the CloverETL Basic Training slides.
The basic course lasts 3 days and covers basic principles, CloverETL Designer walkthrough, transaction analysis, lookups, database connections, working with structured data, XML etc.
More at www.cloveretl.com/services/training
Tony Scalese, Edgewater Ranzal Oracle Financial Data Management (FDM) practice director, presented "Getting the Most Out of FDMEE in a Multiproduct Environment" at KScope14.
JavaOne BOF 5957 Lightning Fast Access to Big DataBrian Martin
Traditionally data is placed in storage and then, when needed, accessed and acted upon in memory. This results in a natural bottleneck that degrades performance. Today, with in-memory computing, we can take advantage of a better understanding of how data is shaped and stored. In this session, you will learn how in-memory data grids mark an inflection point for enterprise applications, especially in dealing with big data. The session covers how large data sets can be made available and can be accessed nearly instantaneously.
HFM API Deep Dive – Making a Better Financial Management ClientCharles Beyer
While the Financial Management Client program that ships with HFM provides key functionality is quite reliable, there are many areas where it could be improved. Fortunately for us, the Financial Management Client is a great example of a program that implements the HFM COM API as many of the program’s features are directly attributable to API functions. Due to this fact, we can focus on adding value without having to completely reinvent the wheel.
This session, which builds off of a previous API presentation, will further walk you through a complete program build utilizing HFM COM API functionality and Microsoft Visual Studio .NET. Fully working code will be provided as well for viewers.
The Teton Project, an ongoing Open Architecture (OA) initiative at Northrop Grumman Electronic Systems (NGES), has adopted the new OMG "DDS for Lightweight CCM" (DDS4CCM) standard as the foundational real-time component framework technology for its Scalable Node Architecture (SNA). SNA is targeted at distributed, real-time, embedded (DRE) and high performance computing (HPC) applications and subsystem/system architectures. This presentation offers the Teton Project's perspective on the advantages of DDS4CCM, a.k.a. Component Based DDS (CBDDS), over a straight DDS-based architectural approach.
JavaOne2013: Implement a High Level Parallel API - Richard NingChris Bailey
This session discusses how to implement a high-level parallel API (such as parallel_for, parallel_while, or parallel_scan) and math calculation based on a thread pool and task in OpenJDK that aligns with the development of multicores and parallel computing. At present, programmers have to use a schedule strategy statically in code instead of choosing it dynamically based on the core number and load balance on the computer with the current Java concurrent package. In the design presented in the session, the function parallel_for(array, task) is a high-level API that can divide the task range dynamically, based on the condition of and load on different computers.
Presented by Richard Ning at JavaOne 2013
026 Neo4j Data Loading (ETL_ELT) Best Practices - NODES2022 AMERICAS Advanced...Neo4j
What patterns are most appropriate for building ETLs using Neo4j? In this session, we share how we built the Google Cloud DataFlow flex template using the Neo4j Java API. You can then apply the same approach to building read and write operators in any framework, including AWS Lambda and Google Cloud Functions.
Speech of Dmytro Shapovalov, Infrastructure Engineer at Cossack Labs, at Ruby Meditation #26 Kyiv 16.02.2019
Next conference - http://www.rubymeditation.com/
Most modern applications live in a close cooperation with each other. We will talk about the ways to effectively use the modern techniques for monitoring the health of applications and look on tasks and typical implementation mistakes through the eyes of an infrastructure engineer. And we will also consider the Ruby libraries that help to implement all of this.
Announcements and conference materials https://www.fb.me/RubyMeditation
News https://twitter.com/RubyMeditation
Photos https://www.instagram.com/RubyMeditation
The stream of Ruby conferences (not just ours) https://t.me/RubyMeditation
(ATS3-DEV04) Introduction to Pipeline Pilot Protocol Development for DevelopersBIOVIA
An overview of techniques for building Pipeline Pilot protocols, using the languages and paradigms familiar to software developers. Sound engineering principles should be applied to the development of protocols, so this session will discuss concepts like modularity and re-use, minimizing side effects, clarity of interfaces, multi-threading, version control. We will also cover the data pipelining architecture of Pipeline Pilot and how that affects the approach to protocol authoring.
JavaOne BOF 5957 Lightning Fast Access to Big DataBrian Martin
Traditionally data is placed in storage and then, when needed, accessed and acted upon in memory. This results in a natural bottleneck that degrades performance. Today, with in-memory computing, we can take advantage of a better understanding of how data is shaped and stored. In this session, you will learn how in-memory data grids mark an inflection point for enterprise applications, especially in dealing with big data. The session covers how large data sets can be made available and can be accessed nearly instantaneously.
HFM API Deep Dive – Making a Better Financial Management ClientCharles Beyer
While the Financial Management Client program that ships with HFM provides key functionality is quite reliable, there are many areas where it could be improved. Fortunately for us, the Financial Management Client is a great example of a program that implements the HFM COM API as many of the program’s features are directly attributable to API functions. Due to this fact, we can focus on adding value without having to completely reinvent the wheel.
This session, which builds off of a previous API presentation, will further walk you through a complete program build utilizing HFM COM API functionality and Microsoft Visual Studio .NET. Fully working code will be provided as well for viewers.
The Teton Project, an ongoing Open Architecture (OA) initiative at Northrop Grumman Electronic Systems (NGES), has adopted the new OMG "DDS for Lightweight CCM" (DDS4CCM) standard as the foundational real-time component framework technology for its Scalable Node Architecture (SNA). SNA is targeted at distributed, real-time, embedded (DRE) and high performance computing (HPC) applications and subsystem/system architectures. This presentation offers the Teton Project's perspective on the advantages of DDS4CCM, a.k.a. Component Based DDS (CBDDS), over a straight DDS-based architectural approach.
JavaOne2013: Implement a High Level Parallel API - Richard NingChris Bailey
This session discusses how to implement a high-level parallel API (such as parallel_for, parallel_while, or parallel_scan) and math calculation based on a thread pool and task in OpenJDK that aligns with the development of multicores and parallel computing. At present, programmers have to use a schedule strategy statically in code instead of choosing it dynamically based on the core number and load balance on the computer with the current Java concurrent package. In the design presented in the session, the function parallel_for(array, task) is a high-level API that can divide the task range dynamically, based on the condition of and load on different computers.
Presented by Richard Ning at JavaOne 2013
026 Neo4j Data Loading (ETL_ELT) Best Practices - NODES2022 AMERICAS Advanced...Neo4j
What patterns are most appropriate for building ETLs using Neo4j? In this session, we share how we built the Google Cloud DataFlow flex template using the Neo4j Java API. You can then apply the same approach to building read and write operators in any framework, including AWS Lambda and Google Cloud Functions.
Speech of Dmytro Shapovalov, Infrastructure Engineer at Cossack Labs, at Ruby Meditation #26 Kyiv 16.02.2019
Next conference - http://www.rubymeditation.com/
Most modern applications live in a close cooperation with each other. We will talk about the ways to effectively use the modern techniques for monitoring the health of applications and look on tasks and typical implementation mistakes through the eyes of an infrastructure engineer. And we will also consider the Ruby libraries that help to implement all of this.
Announcements and conference materials https://www.fb.me/RubyMeditation
News https://twitter.com/RubyMeditation
Photos https://www.instagram.com/RubyMeditation
The stream of Ruby conferences (not just ours) https://t.me/RubyMeditation
(ATS3-DEV04) Introduction to Pipeline Pilot Protocol Development for DevelopersBIOVIA
An overview of techniques for building Pipeline Pilot protocols, using the languages and paradigms familiar to software developers. Sound engineering principles should be applied to the development of protocols, so this session will discuss concepts like modularity and re-use, minimizing side effects, clarity of interfaces, multi-threading, version control. We will also cover the data pipelining architecture of Pipeline Pilot and how that affects the approach to protocol authoring.
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The 2.0 Component Model contains a hierarchical structure of XML objects used to enable 2.0 APIs and web services which subsequently allows developers to optimize component reuse and build other 2.0 XML objects.
ConFoo Montreal - Microservices for building an IDE - The innards of JetBrain...Maarten Balliauw
Ever wondered how IDE’s are built? In this talk, we’ll skip the marketing bit and dive into the architecture and implementation of JetBrains Rider. We’ll look at how and why we have built (and open sourced) a reactive protocol, and how the IDE uses a “microservices” architecture to communicate with the debugger, Roslyn, a WPF renderer and even other tools like Unity3D. We’ll explore how things are wired together, both in-process and across those microservices. Let’s geek out!
(ATS3-PLAT07) Pipeline Pilot Protocol Tips, Tricks, and ChallengesBIOVIA
This session will dive deep into the bowels of the pro client. You will learn how to get the most out of recent enhancements such as design mode and protocol comparison. Protocol authors will also learn about best practices for the use of subprotocols, shortcuts, and search. Protocol developers will also get a better understanding of important but not widely known features in the pro client for deployment, collaboration, and validation. If you want to take your protocol development skills to the next level this is the session to attend!
CloverETL fi ts Tableau user needs when
streamlined data prep is needed, due to
complexity of data flows, demand for
data quality and performance scalability.
This chart explains how CloverETL makes the work of data integration fast and easy. Paired with Tableau's business intelligence and analytics software, it can help you to gather, filter, cleanse, transform, and enrich data before using Tableau to visualize it.
We provide the essence. Data.
For more information visit http://www.cloveretl.com/tableau
This presenation explains basics of ETL (Extract-Transform-Load) concept in relation to such data solutions as data warehousing, data migration, or data integration. CloverETL is presented closely as an example of enterprise ETL tool. It also covers typical phases of data integration projects.
More from CloverDX (formerly known as CloverETL) (6)
CloverETL product family can easily fit different usage scenarios:Open source CloverETL Engine for very small or hobby projectsStandalone CloverETL Designer for small projectsCloverETL Server (includes Designer) for medium to large projectsCloverETL Enterprise Server for large projects, optionally can support clustering for even better performance
Each metadata defines a record structure which is used when parsing the data in reader components or writing the output in writer components. To make the work easier, each record has its own name. Note that the name does not have to be unique within a graph – Clover uses internal identifiers (metadata id) to distinguish between different records with the same name. It is however strongly recommended that the record names are unique to prevent confusion during the development of larger graphs.Each record name has to be a valid identifier and therefore can only contain letters, numbers and underscores. Record names are case-sensitive when used in code.Each record can contain any number of fields of various types. Fields only have simple types and it is not possible to nest records into each other like in Java or other popular languages. Each field has to have a name which is unique within the record. Field names have to be identifiers as well and therefore they have to conform to the same rules as record names.
Record types:Delimited: whole record defines a delimiter between records and each field can have its own delimiter which separates it from the next field. Clover supports delimiters with multiple characters and each delimiter can be different.Fixed-length: each field has predefined width and no delimiters are used.Mixed: some of the fields are delimited and some of the fields have fixed length.Field types:boolean: simple true/false valueinteger: signed integer number, 32-bit (minimum value is -2 147 483 648; maximum is 2 147 483 647)long: signed integer number, 64-bit (minimum is -9 223 372 036 854 775 808; maximum is -9 223 372 036 854 775 807)number: a floating-point number (64-bit IEEE 754 double precision, same as Java double data type).string: a character string. All strings are unicode and are represented in UTF-16. The maximum length of a string is 2^32-1 (the maximum value of integer).date: represents a date with millisecond precision. Note that it is possible to specify the date formatting via custom format string. Clover supports two libraries for date formatting – built-in Java standard library and Joda time library. More details about the formatting options provided by these libraries can be found online:Built-in Java library: formatting performed by java.util.SimpleDateFormat class, online documentation at http://docs.oracle.com/javase/7/docs/api/java/text/SimpleDateFormat.htmlJoda time: formatting performed by org.joda.time.format.DateTimeFormatter class with configuration specified as DateTimeFormat class, online documentation for formatting strings can be found at http://joda-time.sourceforge.net/apidocs/org/joda/time/format/DateTimeFormat.htmldecimal: a fixed-precision number with configurable precision. Two parameters – length and scale – define the total number of digits and the number of digits after decimal point.byte, cbyte: array of bytes. cbyte is compressed in memory so it can be used for larger data.It is also possible to use containers for field values. Clover supports lists and maps of primitive data types. For maps, the map is always map[string, X] where X is the data type specified in the metadata. Note that not all components support these types in full (for example it is not possible to use them as keys for sorting etc.).
Programmable components in CloverETL all use set of functions which are called in a specific order when processing the data. In general, the components contain initialization functions (init and preExecute) which are called before the record processing starts. Then usually one “main” function (like transform or generate) which is called once for each incoming record. Finally, after all data is processed, postExecute is called.Only the main function is mandatory – other functions (initialization and post-processing) are optional and you do not need to provide their implementations. All onError functions (like transformOnError) are optional as well – if they are not specified, the processing fails whenever an error is encountered.Some of the components (e.g. RollUp) contain multiple functions which are called for each record. See later modules for more details on advanced programming in Clover.The function prototypes are automatically created for you when you create new transformation (i.e. when you first try to open transformation code after adding a component to the graph). Optional functions are commented out in the source and you can uncomment them if needed.
Onlytransform and append functions are mandatory. All other functions mentioned on the slide can be left unimplemented.Note: it is not possible to access fields from the input record in transform function. The transformation will crash if you attempt to use anything from the input port. It is therefore necessary to store all the data you need in a variable. For example, it is possible to create instance of a record and copy the data via wildcard mappings:InputMetadata temp;temp.* = $in.0.*;InputMetadata is of course name of the metadata coming through the input port.Note: it is not possible to access output ports in append function.