Power BI Charts Tutorial | Counter Strike Data Analysis using Power BI | Powe...Edureka!
?
** Power B Training: https://www.edureka.co/power-bi-training **
This Edureka Tutorial on "Power BI Charts" deals with the importance of all the basic visualizations available on Power BI Desktop. It will help you create Impactful and Comprehensive Reports on the Power BI Desktop.
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
El documento describe los diferentes componentes de Alfresco para la gestión documental, incluyendo el Web Editor, Share, Record Management y WCM. Share permite la creación de espacios colaborativos como wikis, blogs y calendarios para la gestión de contenidos y documentos.
Applications have to be integrated – no matter which programming languages, databases or infrastructures are used. However, the realization of integration scenarios is a complex and time-consuming task. Over 10 years ago, Enteprise Integration Patterns (EIP) became the world wide defacto standard for splitting huge, complex integration scenarios into smaller recurring problems. These patterns appear in almost every integration project.
This session revisits EIPs and gives shows status quo. After giving a short introduction with several examples, the audience will learn which EIPs still have a ?right to exist“, and which new EIPs emerged in the meantime. The end of the session shows different frameworks and tools which already implement EIPs and therefore help the architect to reduce efforts a lot.
Presented at the MLConf in Seattle, this presentation offers a quick introduction to Apache Spark, followed by an overview of two novel features for data science
Neo4j: The path to success with Graph Database and Graph Data ScienceNeo4j
?
This document provides an overview of the Neo4j graph data platform and its capabilities for data science and analytics. It discusses Neo4j's native graph architecture, tools for data scientists and analysts, and how Neo4j enables graph data science across the machine learning lifecycle from feature engineering to model deployment. Algorithms, embeddings, and machine learning pipelines in Neo4j are highlighted. Integration with common data ecosystems is also covered.
Now you can password protect excel outputs too in bi publisherFeras Ahmad
?
Oracle introduced password protection for Excel 2007 outputs in its 18c update, allowing reports generated in BI Publisher to be password protected when downloaded as Excel files. Previously, password protection was only available for PDF outputs and there were bugs limiting its use for Excel. To enable it, users navigate to Reports and Analytics in BI Publisher, open a catalog, select a report, set a password, save the report, and run it. When downloading the Excel output, a prompt will appear requiring the password set at the report level. Dynamic passwords using SQL queries are not yet supported for Excel like they are for password protected PDFs.
Ozone is an object store that can be built into HDFS to provide highly scalable object storage capabilities. It uses a hashing algorithm to map object keys to storage containers, which are then distributed across data nodes similarly to HDFS blocks. The storage containers are managed by a storage container manager that maintains metadata about container locations and performs functions like replication. This allows Ozone to provide secure, reliable storage of trillions of objects with a wide range of sizes.
This document discusses how Hadoop can be used in data warehousing and analytics. It begins with an overview of data warehousing and analytical databases. It then describes how organizations traditionally separate transactional and analytical systems and use extract, transform, load processes to move data between them. The document proposes using Hadoop as an alternative to traditional data warehousing architectures by using it for extraction, transformation, loading, and even serving analytical queries.
Presented to The Ottawa IT Community Meetup Group (Ottawa SQL - PASS Chapter) on Thursday September 19
Powerful Self-Service BI in Excel 2013 - Data search and discovery with Power Query (formerly "Data Explorer"), analyzing and modeling with Power Pivot, visualizing and exploring with Power View and Power Map (formerly codename "GeoFlow")
Power BI is a business analytics service that allows users to analyze data and share insights. It includes dashboards, reports, and datasets that can be viewed on mobile devices. Power BI integrates with various data sources and platforms like SQL Server, Azure, and Office 365. It provides self-service business intelligence capabilities for end users to explore and visualize data without assistance from IT departments.
The Parquet Format and Performance Optimization OpportunitiesDatabricks
?
The Parquet format is one of the most widely used columnar storage formats in the Spark ecosystem. Given that I/O is expensive and that the storage layer is the entry point for any query execution, understanding the intricacies of your storage format is important for optimizing your workloads.
As an introduction, we will provide context around the format, covering the basics of structured data formats and the underlying physical data storage model alternatives (row-wise, columnar and hybrid). Given this context, we will dive deeper into specifics of the Parquet format: representation on disk, physical data organization (row-groups, column-chunks and pages) and encoding schemes. Now equipped with sufficient background knowledge, we will discuss several performance optimization opportunities with respect to the format: dictionary encoding, page compression, predicate pushdown (min/max skipping), dictionary filtering and partitioning schemes. We will learn how to combat the evil that is ‘many small files’, and will discuss the open-source Delta Lake format in relation to this and Parquet in general.
This talk serves both as an approachable refresher on columnar storage as well as a guide on how to leverage the Parquet format for speeding up analytical workloads in Spark using tangible tips and tricks.
InterPlanetary Linked Data (IPLD) is the data layer for content-addressed systems and the Web3.0. It is a suite of technologies for representing and traversing hash-linked data. In this module you will understand:
- Why IPLD exists
- IPLD’s fundamental concepts, such as Merkle DAGs and Merkle Roots
- The relation of IPLD to IPFS
- How to use IPLD for a distributed data structure.
IBM InfoSphere Data Architect 9.1 - Francis ArnaudièsIBMInfoSphereUGFR
?
The document discusses IBM InfoSphere Data Architect, a tool for modeling, relating, and standardizing diverse data assets. It can design and manage enterprise data models, enforce standards, leverage industry data models, and optimize existing investments. The tool is based on the Eclipse platform and allows various users like data architects, database developers, and administrators to be more productive. It provides logical, physical, and dimensional modeling capabilities as well as tools to define and enforce standards to increase quality and governance.
This document discusses the future of data and the Azure data ecosystem. It highlights that by 2025 there will be 175 zettabytes of data in the world and the average person will have over 5,000 digital interactions per day. It promotes Azure services like Power BI, Azure Synapse Analytics, Azure Data Factory and Azure Machine Learning for extracting value from data through analytics, visualization and machine learning. The document provides overviews of key Azure data and analytics services and how they fit together in an end-to-end data platform for business intelligence, artificial intelligence and continuous intelligence applications.
This document provides an overview of Apache HBase, including:
- Two presenters from Cloudera will discuss HBase's architecture, data model, and hands-on installation and usage.
- HBase is an open-source, distributed, scalable database built on Hadoop that allows for random, real-time read/write access to big data.
- The presentation will cover HBase fundamentals, demonstrate its usage, and discuss how companies apply it for large-scale analytics and real-time applications.
Real-Time Spark: From Interactive Queries to StreamingDatabricks
?
This document summarizes Michael Armbrust's presentation on real-time Spark. It discusses:
1. The goals of real-time analytics including having the freshest answers as fast as possible while keeping the answers up to date.
2. Spark 2.0 introduces unified APIs for SQL, DataFrames and Datasets to make developing real-time analytics simpler with powerful yet simple APIs.
3. Structured streaming allows running the same SQL queries on streaming data to continuously aggregate data and update outputs, unifying batch, interactive, and streaming queries into a single API.
DAX and Power BI Training - 001 OverviewWill Harvey
?
Course & Power BI Overview: This is the first session in a course that primarily focuses on DAX and PowerPivot, but also teaches the surrounding tools such as Power Query, Power BI Desktop and PowerBI.com.
Este documento discute el nombre verdadero de Jesucristo. Argumenta que el nombre verdadero en hebreo es Yahoshua y no Jesús, y que este último nombre fue introducido por Roma para blasfemar el verdadero nombre de salvación. También sugiere que Jesús es un nombre pagano que significa "aquí está el caballo" en hebreo, mientras que proporciona etimologías alternativas para argumentar que Jesús significa "este cerdo terminado". El documento insta a los lectores a rechazar el nombre de Jesús
Azure Data Factory can now use Mapping Data Flows to orchestrate ETL workloads. Mapping Data Flows allow users to visually design transformations on data from disparate sources and load the results into Azure SQL Data Warehouse for analytics. The key benefits of Mapping Data Flows are that they provide a visual interface for building expressions to cleanse and join data with auto-complete assistance and live previews of expression results.
In a world where compute is paramount, it is all too easy to overlook the importance of storage and IO in the performance and optimization of Spark jobs.
Transformations and actions a visual guide trainingSpark Summit
?
The document summarizes key Spark API operations including transformations like map, filter, flatMap, groupBy, and actions like collect, count, and reduce. It provides visual diagrams and examples to illustrate how each operation works, the inputs and outputs, and whether the operation is narrow or wide.
Pig Tutorial | Twitter Case Study | Apache Pig Script and Commands | EdurekaEdureka!
?
This Edureka Pig Tutorial ( Pig Tutorial Blog Series: https://goo.gl/KPE94k ) will help you understand the concepts of Apache Pig in depth.
Check our complete Hadoop playlist here: https://goo.gl/ExJdZs
Below are the topics covered in this Pig Tutorial:
1) Entry of Apache Pig
2) Pig vs MapReduce
3) Twitter Case Study on Apache Pig
4) Apache Pig Architecture
5) Pig Components
6) Pig Data Model
7) Running Pig Commands and Pig Scripts (Log Analysis)
Presented to The Ottawa IT Community Meetup Group (Ottawa SQL - PASS Chapter) on Thursday September 19
Powerful Self-Service BI in Excel 2013 - Data search and discovery with Power Query (formerly "Data Explorer"), analyzing and modeling with Power Pivot, visualizing and exploring with Power View and Power Map (formerly codename "GeoFlow")
Power BI is a business analytics service that allows users to analyze data and share insights. It includes dashboards, reports, and datasets that can be viewed on mobile devices. Power BI integrates with various data sources and platforms like SQL Server, Azure, and Office 365. It provides self-service business intelligence capabilities for end users to explore and visualize data without assistance from IT departments.
The Parquet Format and Performance Optimization OpportunitiesDatabricks
?
The Parquet format is one of the most widely used columnar storage formats in the Spark ecosystem. Given that I/O is expensive and that the storage layer is the entry point for any query execution, understanding the intricacies of your storage format is important for optimizing your workloads.
As an introduction, we will provide context around the format, covering the basics of structured data formats and the underlying physical data storage model alternatives (row-wise, columnar and hybrid). Given this context, we will dive deeper into specifics of the Parquet format: representation on disk, physical data organization (row-groups, column-chunks and pages) and encoding schemes. Now equipped with sufficient background knowledge, we will discuss several performance optimization opportunities with respect to the format: dictionary encoding, page compression, predicate pushdown (min/max skipping), dictionary filtering and partitioning schemes. We will learn how to combat the evil that is ‘many small files’, and will discuss the open-source Delta Lake format in relation to this and Parquet in general.
This talk serves both as an approachable refresher on columnar storage as well as a guide on how to leverage the Parquet format for speeding up analytical workloads in Spark using tangible tips and tricks.
InterPlanetary Linked Data (IPLD) is the data layer for content-addressed systems and the Web3.0. It is a suite of technologies for representing and traversing hash-linked data. In this module you will understand:
- Why IPLD exists
- IPLD’s fundamental concepts, such as Merkle DAGs and Merkle Roots
- The relation of IPLD to IPFS
- How to use IPLD for a distributed data structure.
IBM InfoSphere Data Architect 9.1 - Francis ArnaudièsIBMInfoSphereUGFR
?
The document discusses IBM InfoSphere Data Architect, a tool for modeling, relating, and standardizing diverse data assets. It can design and manage enterprise data models, enforce standards, leverage industry data models, and optimize existing investments. The tool is based on the Eclipse platform and allows various users like data architects, database developers, and administrators to be more productive. It provides logical, physical, and dimensional modeling capabilities as well as tools to define and enforce standards to increase quality and governance.
This document discusses the future of data and the Azure data ecosystem. It highlights that by 2025 there will be 175 zettabytes of data in the world and the average person will have over 5,000 digital interactions per day. It promotes Azure services like Power BI, Azure Synapse Analytics, Azure Data Factory and Azure Machine Learning for extracting value from data through analytics, visualization and machine learning. The document provides overviews of key Azure data and analytics services and how they fit together in an end-to-end data platform for business intelligence, artificial intelligence and continuous intelligence applications.
This document provides an overview of Apache HBase, including:
- Two presenters from Cloudera will discuss HBase's architecture, data model, and hands-on installation and usage.
- HBase is an open-source, distributed, scalable database built on Hadoop that allows for random, real-time read/write access to big data.
- The presentation will cover HBase fundamentals, demonstrate its usage, and discuss how companies apply it for large-scale analytics and real-time applications.
Real-Time Spark: From Interactive Queries to StreamingDatabricks
?
This document summarizes Michael Armbrust's presentation on real-time Spark. It discusses:
1. The goals of real-time analytics including having the freshest answers as fast as possible while keeping the answers up to date.
2. Spark 2.0 introduces unified APIs for SQL, DataFrames and Datasets to make developing real-time analytics simpler with powerful yet simple APIs.
3. Structured streaming allows running the same SQL queries on streaming data to continuously aggregate data and update outputs, unifying batch, interactive, and streaming queries into a single API.
DAX and Power BI Training - 001 OverviewWill Harvey
?
Course & Power BI Overview: This is the first session in a course that primarily focuses on DAX and PowerPivot, but also teaches the surrounding tools such as Power Query, Power BI Desktop and PowerBI.com.
Este documento discute el nombre verdadero de Jesucristo. Argumenta que el nombre verdadero en hebreo es Yahoshua y no Jesús, y que este último nombre fue introducido por Roma para blasfemar el verdadero nombre de salvación. También sugiere que Jesús es un nombre pagano que significa "aquí está el caballo" en hebreo, mientras que proporciona etimologías alternativas para argumentar que Jesús significa "este cerdo terminado". El documento insta a los lectores a rechazar el nombre de Jesús
Azure Data Factory can now use Mapping Data Flows to orchestrate ETL workloads. Mapping Data Flows allow users to visually design transformations on data from disparate sources and load the results into Azure SQL Data Warehouse for analytics. The key benefits of Mapping Data Flows are that they provide a visual interface for building expressions to cleanse and join data with auto-complete assistance and live previews of expression results.
In a world where compute is paramount, it is all too easy to overlook the importance of storage and IO in the performance and optimization of Spark jobs.
Transformations and actions a visual guide trainingSpark Summit
?
The document summarizes key Spark API operations including transformations like map, filter, flatMap, groupBy, and actions like collect, count, and reduce. It provides visual diagrams and examples to illustrate how each operation works, the inputs and outputs, and whether the operation is narrow or wide.
Pig Tutorial | Twitter Case Study | Apache Pig Script and Commands | EdurekaEdureka!
?
This Edureka Pig Tutorial ( Pig Tutorial Blog Series: https://goo.gl/KPE94k ) will help you understand the concepts of Apache Pig in depth.
Check our complete Hadoop playlist here: https://goo.gl/ExJdZs
Below are the topics covered in this Pig Tutorial:
1) Entry of Apache Pig
2) Pig vs MapReduce
3) Twitter Case Study on Apache Pig
4) Apache Pig Architecture
5) Pig Components
6) Pig Data Model
7) Running Pig Commands and Pig Scripts (Log Analysis)
12. IF 三招
? IF ERRORLEVEL 3
– exit code >= 3
? IF foo == bar
– 陷阱很多
? IF EXIST file
所以要從大的挑到小的。
12
13. 陷阱:小心 空變數 與 空白字元 (1)
SET VAR=
IF %VAR% == foo (
ECHO OK
)
這個時候不應有 (。
IF == foo (
SET VAR=foo bar
IF _%VAR% == _foo (
ECHO OK
)
這個時候不應有 bar。
IF _foo bar == _foo (
IF _ == _foo ( IF "foo bar" == foo (
13
14. 陷阱:小心 空變數 與 空白字元 (2)
SET VAR=foo bar
IF "%VAR%"=="foo bar" (
ECHO OK
)
OK
IF "foo bar" == "foo bar" (
SET VAR="foo bar"
IF "%VAR%"=="foo bar" (
ECHO OK
)
這個時候不應有 bar""=="foo
bar"。
IF ""foo bar""=="foo bar" (
快不行了…只有 %~1 可以去引號算是正解了
正正解:IF "%~1"=="foo" ( 14
15. FOR 四式
FOR … %%I IN (…) DO …
? FOR 清單 (空白分隔的變數)
? FOR /D, FOR /R 檔案 (遞迴)
? FOR /L 數數字
? FOR /F 資料剖析
在 .bat 裡要才 %%
15
16. 應用例:從一加到十
SET SUM=0
FOR /L %%I IN (1,1,10) DO (
SET /A SUM+=%%I
)
ECHO %SUM%
55
(begin, step, end)
SET /A 還能夠算數喔!
16
17. 應用例:讀取設定檔
FOR /F %%I IN (config.ini) DO SET %%I
ECHO apple=%apple%, banana=%banana%, cherry=%cherry%
apple=1, banana=8, cherry=3
config.ini
apple = 1
banana = 8
cherry = 3
17
18. 陷阱:延遲環境變數展開 (需 CMD /V:ON)
SET SUM=0
FOR /L %%I IN (1,1,10) DO (
SET /A SUM+=%%I
ECHO %SUM%
)
0
0
0
0
0
0
0
0
0
0
!SUM!
1
3
6
10
15
21
28
36
45
55
18
23. 更多討論
? PATH 變數內,路徑即使有空白字元也不能加雙引號。
? SET ERRORLEVEL 令 %ERRORLEVEL% 不如往常。
? 含空白字元的字串串接小祕訣
這樣也行:C:"Program Files"Java
? Windows 其實支援 symbolic link 了 (MKLINK /?)
C:ProgramDataOracleJavajavapath 是為一例
? 在 Makefile 裡,指令不能是大寫。
23
24. 衍生閱讀
? 善用 Command /?
– HELP 看支援的指令;SET 看可用的環境變數。
? Rob van der Woude’s Scripting Pages
http://www.robvanderwoude.com/batchfiles.php
? PowerShell
Windows 下一代的腳本語言
24