This document discusses a DataStage scenario problem and its solution design. It involves reading input from a sequential file, passing the data through a transformer stage to create a new column that counts the occurrences of vowels in each name, and writing the output to a file. The output file includes the names from the input and their associated vowel counts.
Pandas is an open source Python library that provides data structures and data analysis tools for working with tabular data. It allows users to easily perform operations on different types of data such as tabular, time series, and matrix data. Pandas provides data structures like Series for 1D data and DataFrame for 2D data. It has tools for data cleaning, transformation, manipulation, and visualization of data.
Building Data Marts – a Sprint Not A Marathon (Forward Intelligence) v5 David Waters
The document discusses agile approaches to data warehousing and business intelligence systems. It advocates for iterative development using short sprints of 2-3 months to deliver useful functionality to users early. This approach focuses on rapid delivery of high priority requirements while still employing good design practices, version control, and testing. The document also outlines the people, processes, and tools needed for agile data warehousing projects, including emphasizing a collaborative team approach and using SAS Enterprise BI tools and Kimball's design patterns.
The document provides an introduction to data warehousing and business intelligence. It discusses how a data warehouse can improve decision making by integrating data from various sources and systems. Key benefits include revenue stimulation, cost reduction, productivity improvement and competitive advantage. The architecture of a data warehouse is described, including how it differs from operational systems in terms of data access, organization and time handling. Dimensional data modeling techniques and performance measures are also covered.
This document describes a DataStage job design to solve a scenario problem. It involves:
1) Reading a sequential file as input and passing the data through a Sort and Transformer stage.
2) The Sort stage sorts the data based on a "Char" column in ascending order.
3) The Transformer stage uses stage variables to calculate an "Occurrence" column showing how many times each character appears.
4) The output file uses inline sorting to sort by the new "Occurrence" column.
This document describes a DataStage job design to solve a scenario problem. The design includes:
1) A job with a sort stage to sort data based on a column in descending order.
2) A transformer stage that uses two stage variables to derive output.
3) An output file that sorts the data a second time in ascending order to produce the required output.
The document discusses designing a DataStage job to solve a scenario problem. It includes:
1) Designing a job with input from a sequential file, sorting by company, and transforming data with stage variables to get the desired output.
2) Properties for the sort and transformer stages.
3) Using a remove duplicate stage set to retain the last record to deduplicate data.
4) The output file contains the transformed and deduplicated data as needed.
This document discusses a DataStage scenario problem and solution involving splitting data from one input file into three separate output files based on a modulus calculation on the input column. The solution involves designing a job with a transformer stage to map the input column to the three output files, and setting constraints on each link based on the modulus calculation to control the data flow to each file. The job is then compiled and run to solve the problem.
Pandas is an open source Python library that provides data structures and data analysis tools for working with tabular data. It allows users to easily perform operations on different types of data such as tabular, time series, and matrix data. Pandas provides data structures like Series for 1D data and DataFrame for 2D data. It has tools for data cleaning, transformation, manipulation, and visualization of data.
Building Data Marts – a Sprint Not A Marathon (Forward Intelligence) v5 David Waters
The document discusses agile approaches to data warehousing and business intelligence systems. It advocates for iterative development using short sprints of 2-3 months to deliver useful functionality to users early. This approach focuses on rapid delivery of high priority requirements while still employing good design practices, version control, and testing. The document also outlines the people, processes, and tools needed for agile data warehousing projects, including emphasizing a collaborative team approach and using SAS Enterprise BI tools and Kimball's design patterns.
The document provides an introduction to data warehousing and business intelligence. It discusses how a data warehouse can improve decision making by integrating data from various sources and systems. Key benefits include revenue stimulation, cost reduction, productivity improvement and competitive advantage. The architecture of a data warehouse is described, including how it differs from operational systems in terms of data access, organization and time handling. Dimensional data modeling techniques and performance measures are also covered.
This document describes a DataStage job design to solve a scenario problem. It involves:
1) Reading a sequential file as input and passing the data through a Sort and Transformer stage.
2) The Sort stage sorts the data based on a "Char" column in ascending order.
3) The Transformer stage uses stage variables to calculate an "Occurrence" column showing how many times each character appears.
4) The output file uses inline sorting to sort by the new "Occurrence" column.
This document describes a DataStage job design to solve a scenario problem. The design includes:
1) A job with a sort stage to sort data based on a column in descending order.
2) A transformer stage that uses two stage variables to derive output.
3) An output file that sorts the data a second time in ascending order to produce the required output.
The document discusses designing a DataStage job to solve a scenario problem. It includes:
1) Designing a job with input from a sequential file, sorting by company, and transforming data with stage variables to get the desired output.
2) Properties for the sort and transformer stages.
3) Using a remove duplicate stage set to retain the last record to deduplicate data.
4) The output file contains the transformed and deduplicated data as needed.
This document discusses a DataStage scenario problem and solution involving splitting data from one input file into three separate output files based on a modulus calculation on the input column. The solution involves designing a job with a transformer stage to map the input column to the three output files, and setting constraints on each link based on the modulus calculation to control the data flow to each file. The job is then compiled and run to solve the problem.
This document discusses using DataStage to solve data integration problems. It describes using sort and filter stages to remove duplicate records from a data set. The sort stage sorts the data on a key column and generates a change key column. The filter stage then filters the data based on the change key, returning each unique record once and any duplicate occurrences.
This document describes a DataStage job design to solve a scenario problem. The design includes:
1) A job with a seq file input, aggregator stage to count rows by key, and filter stage to output rows by count.
2) The aggregator stage groups data by the "No" column and calculates row counts for each key.
3) The filter stage outputs rows where count equals 1 and where count is greater than 1 to separate files.
Best Practices for Building and Deploying Data Pipelines in Apache SparkDatabricks
Many data pipelines share common characteristics and are often built in similar but bespoke ways, even within a single organisation. In this talk, we will outline the key considerations which need to be applied when building data pipelines, such as performance, idempotency, reproducibility, and tackling the small file problem. We’ll work towards describing a common Data Engineering toolkit which separates these concerns from business logic code, allowing non-Data-Engineers (e.g. Business Analysts and Data Scientists) to define data pipelines without worrying about the nitty-gritty production considerations.
We’ll then introduce an implementation of such a toolkit in the form of Waimak, our open-source library for Apache Spark (https://github.com/CoxAutomotiveDataSolutions/waimak), which has massively shortened our route from prototype to production. Finally, we’ll define new approaches and best practices about what we believe is the most overlooked aspect of Data Engineering: deploying data pipelines.
1. The document discusses handling small file problems in Spark ETL pipelines. It recommends keeping partition sizes between 2GB and not too small to avoid overhead problems.
2. It provides examples of transformations like aggregation, normalization, and lookup that are commonly used.
3. Pivoting data in Spark is presented as an efficient solution to transform data compared to traditional ETL tools. The example pivots data to summarize by year and quarter within minutes for billions of records.
This document compares the key differences between Oracle and SQL Server databases. It discusses differences in database structure, terminology, stored procedure languages, and transaction control. It provides comparisons of table types, constraints, indexes, views, data types, functions and more between the two database systems.
The document discusses four models of interaction between humans and machines: 1) Following pre-defined decision trees, 2) Search queries based on keyword intersections, 3) Neural networks using pattern recognition, and 4) "Neural-like" processing that treats data and functions as fused and stateful. It argues that today's methods like relational databases are brittle for natural language queries and that a neural-like approach may enable more flexible and precise answers by representing data and inputs in a neural-like way. The document also contains examples of Java code for integrating data and functions across different applications and systems.
Spark Based Distributed Deep Learning Framework For Big Data Applications Humoyun Ahmedov
Deep Learning architectures, such as deep neural networks, are currently the hottest emerging areas of data science, especially in Big Data. Deep Learning could be effectively exploited to address some major issues of Big Data, such as fast information retrieval, data classification, semantic indexing and so on. In this work, we designed and implemented a framework to train deep neural networks using Spark, fast and general data flow engine for large scale data processing, which can utilize cluster computing to train large scale deep networks. Training Deep Learning models requires extensive data and computation. Our proposed framework can accelerate the training time by distributing the model replicas, via stochastic gradient descent, among cluster nodes for data resided on HDFS.
Avoiding Bad Database Surprises: Simulation and Scalability - Steven LottPyData
The document discusses how to avoid bad database surprises through early simulation and scalability testing. It provides examples of web and analytics apps that did not scale due to unanticipated database issues. It recommends using Python classes and JSON schema to define data models and generate synthetic test data. This allows simulating the full system early in development to identify potential performance bottlenecks before real data is involved.
SQL Server 2000 Research Series - Transact SQLJerry Yang
The document discusses key concepts in Transact-SQL including stored procedures, data types, variables, flow control statements, and functions. It covers topics such as stored procedure design, data type categories, local and global variables, conditional and looping statements, and built-in versus user-defined functions. The summary provides an overview of the document's content for technical integration and SQL training purposes.
The document discusses new developer features introduced in SQL Server 2012-2016, including SSDT tools, T-SQL improvements like THROW and sequences, in-memory OLTP, common table expressions, and features in SQL Server 2016 such as dynamic data masking, row-level security, always encrypted, temporal tables, and JSON support. SQL Server 2016 also introduced the DROP IF EXISTS statement to drop objects and the ability to insert rows using merge statements with common table expressions.
Beyond Shuffling, Tips and Tricks for Scaling Apache Spark updated for Spark ...Holden Karau
Beyond Shuffling - Tips & Tricks for scaling your Apache Spark programs. This talk walks through a number of common mistakes which can keep our Spark programs from scaling and examines the solutions, as well as general techniques useful for moving from beyond a prof of concept to production. It covers topics like effective RDD re-use, considerations for working with key/value data, and finishes up with an introduction to one of Spark's newest features: Datasets.
Quantitative Methods for Lawyers - Class #14 - R Boot Camp - Part 1 - Profess...Daniel Katz
This document provides an introduction to loading datasets in R. It demonstrates how to load a CSV dataset from a URL and assign it to an object name. It shows how to remove rows with missing values and reset the row numbers. The document then covers some basic commands like min, max, mean, and standard deviation. Finally, it demonstrates plotting histograms and box plots of data.
When to NoSQL and When to SQL
NoSQL databases are suited for applications that require rapid development, large data growth, and scale out capabilities. They provide flexible data models like documents and key-value stores. SQL remains effective for query-heavy workloads with complex queries over structured data. A hybrid approach using multiple database types can leverage their respective strengths. The right choice depends on factors like data access patterns, consistency needs, and the skills of those using the system.
The document proposes a distributed deep learning framework for big data applications built on Apache Spark. It discusses challenges in distributed computing and deep learning in big data. The proposed system addresses issues like concurrency, asynchrony, parallelism through a master-worker architecture with data and model parallelism. Experiments on sentiment analysis using word embeddings and deep networks on a 10-node Spark cluster show improved performance with increased nodes.
Yufeng Guo | Coding the 7 steps of machine learning | Codemotion Madrid 2018 Codemotion
Machine learning has gained a lot of attention as the next big thing. But what is it, really, and how can we use it? In this talk, you'll learn the meaning behind buzzwords like hyperparameter tuning, and see the code behind each step of machine learning. This talk will help demystify the "magic" behind machine learning. You'll come away with a foundation that you can build on, and an understanding of the tools to build with!
Jump Start into Apache® Spark™ and DatabricksDatabricks
These are the slides from the Jump Start into Apache Spark and Databricks webinar on February 10th, 2016.
---
Spark is a fast, easy to use, and unified engine that allows you to solve many Data Sciences and Big Data (and many not-so-Big Data) scenarios easily. Spark comes packaged with higher-level libraries, including support for SQL queries, streaming data, machine learning, and graph processing. We will leverage Databricks to quickly and easily demonstrate, visualize, and debug our code samples; the notebooks will be available for you to download.
An overview of two types of graph databases: property databases and knowledge/RDF databases, together with their dominant respective query languages, Cypher and SPARQL. Also a quick look at some property DB frameworks, including TinkerPop and its query language, Gremlin.
This document discusses using DataStage to solve data integration problems. It describes using sort and filter stages to remove duplicate records from a data set. The sort stage sorts the data on a key column and generates a change key column. The filter stage then filters the data based on the change key, returning each unique record once and any duplicate occurrences.
This document describes a DataStage job design to solve a scenario problem. The design includes:
1) A job with a seq file input, aggregator stage to count rows by key, and filter stage to output rows by count.
2) The aggregator stage groups data by the "No" column and calculates row counts for each key.
3) The filter stage outputs rows where count equals 1 and where count is greater than 1 to separate files.
Best Practices for Building and Deploying Data Pipelines in Apache SparkDatabricks
Many data pipelines share common characteristics and are often built in similar but bespoke ways, even within a single organisation. In this talk, we will outline the key considerations which need to be applied when building data pipelines, such as performance, idempotency, reproducibility, and tackling the small file problem. We’ll work towards describing a common Data Engineering toolkit which separates these concerns from business logic code, allowing non-Data-Engineers (e.g. Business Analysts and Data Scientists) to define data pipelines without worrying about the nitty-gritty production considerations.
We’ll then introduce an implementation of such a toolkit in the form of Waimak, our open-source library for Apache Spark (https://github.com/CoxAutomotiveDataSolutions/waimak), which has massively shortened our route from prototype to production. Finally, we’ll define new approaches and best practices about what we believe is the most overlooked aspect of Data Engineering: deploying data pipelines.
1. The document discusses handling small file problems in Spark ETL pipelines. It recommends keeping partition sizes between 2GB and not too small to avoid overhead problems.
2. It provides examples of transformations like aggregation, normalization, and lookup that are commonly used.
3. Pivoting data in Spark is presented as an efficient solution to transform data compared to traditional ETL tools. The example pivots data to summarize by year and quarter within minutes for billions of records.
This document compares the key differences between Oracle and SQL Server databases. It discusses differences in database structure, terminology, stored procedure languages, and transaction control. It provides comparisons of table types, constraints, indexes, views, data types, functions and more between the two database systems.
The document discusses four models of interaction between humans and machines: 1) Following pre-defined decision trees, 2) Search queries based on keyword intersections, 3) Neural networks using pattern recognition, and 4) "Neural-like" processing that treats data and functions as fused and stateful. It argues that today's methods like relational databases are brittle for natural language queries and that a neural-like approach may enable more flexible and precise answers by representing data and inputs in a neural-like way. The document also contains examples of Java code for integrating data and functions across different applications and systems.
Spark Based Distributed Deep Learning Framework For Big Data Applications Humoyun Ahmedov
Deep Learning architectures, such as deep neural networks, are currently the hottest emerging areas of data science, especially in Big Data. Deep Learning could be effectively exploited to address some major issues of Big Data, such as fast information retrieval, data classification, semantic indexing and so on. In this work, we designed and implemented a framework to train deep neural networks using Spark, fast and general data flow engine for large scale data processing, which can utilize cluster computing to train large scale deep networks. Training Deep Learning models requires extensive data and computation. Our proposed framework can accelerate the training time by distributing the model replicas, via stochastic gradient descent, among cluster nodes for data resided on HDFS.
Avoiding Bad Database Surprises: Simulation and Scalability - Steven LottPyData
The document discusses how to avoid bad database surprises through early simulation and scalability testing. It provides examples of web and analytics apps that did not scale due to unanticipated database issues. It recommends using Python classes and JSON schema to define data models and generate synthetic test data. This allows simulating the full system early in development to identify potential performance bottlenecks before real data is involved.
SQL Server 2000 Research Series - Transact SQLJerry Yang
The document discusses key concepts in Transact-SQL including stored procedures, data types, variables, flow control statements, and functions. It covers topics such as stored procedure design, data type categories, local and global variables, conditional and looping statements, and built-in versus user-defined functions. The summary provides an overview of the document's content for technical integration and SQL training purposes.
The document discusses new developer features introduced in SQL Server 2012-2016, including SSDT tools, T-SQL improvements like THROW and sequences, in-memory OLTP, common table expressions, and features in SQL Server 2016 such as dynamic data masking, row-level security, always encrypted, temporal tables, and JSON support. SQL Server 2016 also introduced the DROP IF EXISTS statement to drop objects and the ability to insert rows using merge statements with common table expressions.
Beyond Shuffling, Tips and Tricks for Scaling Apache Spark updated for Spark ...Holden Karau
Beyond Shuffling - Tips & Tricks for scaling your Apache Spark programs. This talk walks through a number of common mistakes which can keep our Spark programs from scaling and examines the solutions, as well as general techniques useful for moving from beyond a prof of concept to production. It covers topics like effective RDD re-use, considerations for working with key/value data, and finishes up with an introduction to one of Spark's newest features: Datasets.
Quantitative Methods for Lawyers - Class #14 - R Boot Camp - Part 1 - Profess...Daniel Katz
This document provides an introduction to loading datasets in R. It demonstrates how to load a CSV dataset from a URL and assign it to an object name. It shows how to remove rows with missing values and reset the row numbers. The document then covers some basic commands like min, max, mean, and standard deviation. Finally, it demonstrates plotting histograms and box plots of data.
When to NoSQL and When to SQL
NoSQL databases are suited for applications that require rapid development, large data growth, and scale out capabilities. They provide flexible data models like documents and key-value stores. SQL remains effective for query-heavy workloads with complex queries over structured data. A hybrid approach using multiple database types can leverage their respective strengths. The right choice depends on factors like data access patterns, consistency needs, and the skills of those using the system.
The document proposes a distributed deep learning framework for big data applications built on Apache Spark. It discusses challenges in distributed computing and deep learning in big data. The proposed system addresses issues like concurrency, asynchrony, parallelism through a master-worker architecture with data and model parallelism. Experiments on sentiment analysis using word embeddings and deep networks on a 10-node Spark cluster show improved performance with increased nodes.
Yufeng Guo | Coding the 7 steps of machine learning | Codemotion Madrid 2018 Codemotion
Machine learning has gained a lot of attention as the next big thing. But what is it, really, and how can we use it? In this talk, you'll learn the meaning behind buzzwords like hyperparameter tuning, and see the code behind each step of machine learning. This talk will help demystify the "magic" behind machine learning. You'll come away with a foundation that you can build on, and an understanding of the tools to build with!
Jump Start into Apache® Spark™ and DatabricksDatabricks
These are the slides from the Jump Start into Apache Spark and Databricks webinar on February 10th, 2016.
---
Spark is a fast, easy to use, and unified engine that allows you to solve many Data Sciences and Big Data (and many not-so-Big Data) scenarios easily. Spark comes packaged with higher-level libraries, including support for SQL queries, streaming data, machine learning, and graph processing. We will leverage Databricks to quickly and easily demonstrate, visualize, and debug our code samples; the notebooks will be available for you to download.
An overview of two types of graph databases: property databases and knowledge/RDF databases, together with their dominant respective query languages, Cypher and SPARQL. Also a quick look at some property DB frameworks, including TinkerPop and its query language, Gremlin.
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DataStage Scenario Problem > DataStage Scenario Problem6
Solution Design :
a) Job Design :
Below is the design which can achieve the output as we needed. Here, we are reading seq file as a input, then data is passing through a
Transformer stage to achieve the output.
b) Transformer Stage Properties
Here, Create a new column in output which contains the Occurrence of characters
and their derivations are
Count : Count(In_xfm.Name,'A')+Count(In_xfm.Name,'E')+Count(In_xfm.Name,'I')
+Count(In_xfm.Name,'O')+Count(In_xfm.Name,'U')+Count(In_xfm.Name,'a')
+Count(In_xfm.Name,'e')+Count(In_xfm.Name,'i')+Count(In_xfm.Name,'o')
+Count(In_xfm.Name,'u')
DataStage Scenario Design6 job1
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