Ruth  Nabimanya

Ruth Nabimanya


ClickHouse: A Free Analytics DBMS for Big Data

ClickHouse® is an open-source column-oriented database management system that allows generating analytical data reports in real-time.

Useful Links

  • Official website has a quick high-level overview of ClickHouse on the main page.
  • Tutorial shows how to set up and query a small ClickHouse cluster.
  • Documentation provides more in-depth information.
  • YouTube channel has a lot of content about ClickHouse in video format.
  • Slack and Telegram allow chatting with ClickHouse users in real-time.
  • Blog contains various ClickHouse-related articles, as well as announcements and reports about events.
  • Code Browser (Woboq) with syntax highlight and navigation.
  • Code Browser ( with syntax highlight, powered by
  • Contacts can help to get your questions answered if there are any.

Download Details:
Author: ClickHouse
Source Code:
License: Apache-2.0 License

#database #cpluplus #python #bigdata 

ClickHouse: A Free Analytics DBMS for Big Data
Dylan  Iqbal

Dylan Iqbal


ICT and Data Sciences (PDF Book for FREE Download)

ICT and Data Sciences brings together IoT and Machine Learning and provides the careful integration of both, along with many examples and case studies. It illustrates the merging of two technologies while presenting basic to high-level concepts covering different fields and domains such as the Hospitality and Tourism industry, Smart Clothing, Cyber Crime, Programming, Communications, Business Intelligence, all in the context of the Internet of Things.

This book highlights the state-of-the-art research on data usage, security, and privacy in the scenarios of the Internet of Things (IoT), along with related applications using Machine Learning and Big Data technologies to design and make efficient Internet-compatible IoT systems.

ICT and Data Sciences by Archana Singh, Ashish Seth, Sai Sabitha, Vinod Kumar Shukla

  • Length: 283 pages
  • Edition: B
  • Language: English
  • Publisher: CRC Pr I Llc
  • Publication Date: 2022-05-12


#datascience #machinelearning #iot #bigdata #ebook #book

ICT and Data Sciences (PDF Book for FREE Download)
Dylan  Iqbal

Dylan Iqbal


Big Data: A Complete Guide To The Basic Concepts (PDF Book for FREE Download)

Big Data: A Complete Guide To The Basic Concepts

Data-governance programs focus on authority and accountability for the management of data as a valued organizational asset. Data Governance should not be about command-and-control, yet at times could become invasive or threatening to the work, people, and culture of an organization. This book focuses on formalizing existing accountability for the management of data and improving formal communications, protection, and quality efforts through effective stewarding of data resources.

The book provides frameworks for business, operational, and technology leaders that are simple and effective in managing data. Many data offices have challenges in actively managing data and deriving consistent value from the data science, Bigdata, and reporting programs. While a few challenges can be cultural, some can be related to the way the people are enabled or toolsets are pulled for self-service. The handbook provides proven approaches to either start managing data with simple frameworks or to mature the existing practices as standard and nimble services.

Big Data: A Complete Guide To The Basic Concepts by Roman Hibbert

  • Length: 138 pages
  • Edition: 1
  • Language: English
  • Publication Date: 2021-12-29


#bigdata #developer #ebook #book 

Big Data: A Complete Guide To The Basic Concepts (PDF Book for FREE Download)
Dylan  Iqbal

Dylan Iqbal


Data Mining Approaches for Big Data and Sentiment Analysis in Social Media (PDF Book for FREE Download)

Data Mining Approaches for Big Data and Sentiment Analysis in Social Media (Advances in Data Mining and Database Management)

Social media sites are constantly evolving with huge amounts of scattered data or big data, which makes it difficult for researchers to trace the information flow. It is a daunting task to extract a useful piece of information from the vast unstructured big data; the disorganized structure of social media contains data in various forms such as text and videos as well as huge real-time data on which traditional analytical methods like statistical approaches fail miserably. Due to this, there is a need for efficient data mining techniques that can overcome the shortcomings of the traditional approaches.

Data Mining Approaches for Big Data and Sentiment Analysis in Social Media encourages researchers to explore the key concepts of data mining, such as how they can be utilized on online social media platforms, and provides advances on data mining for big data and sentiment analysis in online social media, as well as future research directions. Covering a range of concepts from machine learning methods to data mining for big data analytics, this book is ideal for graduate students, academicians, faculty members, scientists, researchers, data analysts, social media analysts, managers, and software developers who are seeking to learn and carry out research in the area of data mining for big data and sentiment.

Data Mining Approaches for Big Data and Sentiment Analysis in Social Media by Ahmed A. Abd El-Latif, Brij B. Gupta, Dragan Perakovic


#datamining #bigdata #analysis #developer #ebook #book 

Data Mining Approaches for Big Data and Sentiment Analysis in Social Media (PDF Book for FREE Download)
Dylan  Iqbal

Dylan Iqbal


Building Big Data Pipelines with Apache Beam (PDF Book for FREE Download)

Implement, run, operate, and test data processing pipelines using Apache Beam

Key Features

  • Understand how to improve usability and productivity when implementing Beam pipelines
  • Learn how to use stateful processing to implement complex use cases using Apache Beam
  • Implement, test, and run Apache Beam pipelines with the help of expert tips and techniques

Book Description

Apache Beam is an open source unified programming model for implementing and executing data processing pipelines, including Extract, Transform, and Load (ETL), batch, and stream processing.

This book will help you to confidently build data processing pipelines with Apache Beam. You'll start with an overview of Apache Beam and understand how to use it to implement basic pipelines. You'll also learn how to test and run the pipelines efficiently. As you progress, you'll explore how to structure your code for reusability and also use various Domain Specific Languages (DSLs). Later chapters will show you how to use schemas and query your data using (streaming) SQL. Finally, you'll understand advanced Apache Beam concepts, such as implementing your own I/O connectors.

By the end of this book, you'll have gained a deep understanding of the Apache Beam model and be able to apply it to solve problems.

What you will learn

  • Understand the core concepts and architecture of Apache Beam
  • Implement stateless and stateful data processing pipelines
  • Use state and timers for processing real-time event processing
  • Structure your code for reusability
  • Use streaming SQL to process real-time data for increasing productivity and data accessibility
  • Run a pipeline using a portable runner and implement data processing using the Apache Beam Python SDK
  • Implement Apache Beam I/O connectors using the Splittable DoFn API

Who this book is for

This book is for data engineers, data scientists, and data analysts who want to learn how Apache Beam works. Intermediate-level knowledge of the Java programming language is assumed.

Table of Contents

  1. Introduction to Data Processing with Apache Beam
  2. Implementing, Testing, and Deploying Basic Pipelines
  3. Implementing Pipelines Using Stateful Processing
  4. Structuring Code for Reusability
  5. Using SQL for Pipeline Implementation
  6. Using Your Preferred Language with Portability
  7. Extending Apache Beam's I/O Connectors
  8. Understanding How Runners Execute Pipelines


#bigdata #apachebeam #apache #developer #ebook #book

Building Big Data Pipelines with Apache Beam (PDF Book for FREE Download)
Dylan  Iqbal

Dylan Iqbal


Logic-Driven Traffic Big Data Analytics (PDF Book for FREE Download)

Logic-Driven Traffic Big Data Analytics: Methodology and Applications for Planning 1st ed. 2022 Edición

This book starts from the relationship between urban built environment and travel behavior and focuses on analyzing the origin of traffic phenomena behind the data through multi-source traffic big data, which makes the book unique and different from the previous data-driven traffic big data analysis literature. This book focuses on understanding, estimating, predicting, and optimizing mobility patterns. Readers can find multi-source traffic big data processing methods, related statistical analysis models, and practical case applications from this book.

This book bridges the gap between traffic big data, statistical analysis models, and mobility pattern analysis with a systematic investigation of traffic big data’s impact on mobility patterns and urban planning.



#bigdata #dataanalytics #ebook #book #pdf 

Logic-Driven Traffic Big Data Analytics (PDF Book for FREE Download)
Gunjan  Khaitan

Gunjan Khaitan


Hadoop Tutorial for Beginners - Full Course

Hadoop Tutorial For Beginners 2022 | Hadoop Full Course In 10 Hours | Big Data Tutorial

This full course video on Hadoop will introduce you to the world of big data, the applications of big data, the significant challenges in big data, and how Hadoop solves these major challenges. You will get an idea about the essential tools that are part of the Hadoop ecosystem. You will learn how Hadoop stores vast volumes of data using HDFS, and processes this data using MapReduce. You will understand how cluster resource management works using YARN. It will make you know how you can query and analyze big data using tools and frameworks like Hive, Pig, Sqoop, and HBase. These of these tools will give a hands-on experience that will help you understand it better. Finally, you will see how to become a big data engineer and come across a few important interview questions to build your career in Hadoop. Now, let's get started and learn Hadoop.

The below topics are covered in this Hadoop full course tutorial:

  • Evolution of Big Data
  • Why Big Data
  • What is Big Data
  • 5V's of Big Data
  • Big Data Case Study
  • Challenges of Big Data
  • Hadoop as a Solutions
  • History of Hadoop
  • Cloudera Hadoop Installation
  • Hadoop Installation on Ubuntu
  • Hadoop Ecosystem
  • HDFS Tutorial
  • Why HDFS?
  • What is HDFS? 
  • HDFS Cluster Architecture
  • HDFS Data Blocks
  • DataNode Failure and Replication
  • Rack Awareness in HDFS
  • HDFS Architecture
  • HDFS Read Mechanism
  • HDFS Write Mechanism
  • HDFS Write Mechanism with example
  • Advantages of HDFS
  • HDFS Tutorial
  • MapReduce Analogy
  • What is MapReduce?
  • Parallel Processing MapReduce
  • MapReduce Workflow
  • MapReduce Architecture
  • MapReduce Example
  • Hadoop 1.0 (MR 1)
  • Limitations of Hadoop 1.0 (MR 1)
  • Need for YARN - 4:02:25
  • Solution - Hadoop 2.0 (YARN) - 4:04:15
  • What is YARN? - 4:05:13
  • Workloads running on YARN - 4:06:33
  • YARN Components
  • YARN Components - Resource Manager
  • YARN Components - Node Manager
  • YARN Architecture
  • Running an application in YARN
  • Need for Sqoop
  • What is Sqoop
  • Sqoop Features
  • Sqoop Architecture
  • Sqoop Import
  • Sqoop Export
  • Sqoop Processing
  • Demo on Sqoop
  • Flume
  • Hadoop Ecosystem
  • History of Hive
  • Big Data Analytics
  • Big Data Applications
  • How to become a Big Data Engineer
  • Hadoop Interview Questions

#hadoop #bigdata 

Hadoop Tutorial for Beginners - Full Course
Dylan  Iqbal

Dylan Iqbal


Python for Programmers: Big Data and Artificial Intelligence Case Studies (PDF Book for FREE Download)

Python for Programmers: with Big Data and Artificial Intelligence Case Studies by Paul Deitel , Harvey Deitel is a PDF book for free download.

The professional programmer’s Deitel guide to Pythonwith introductory artificial intelligence case studies

Written for programmers with a background in another high-level language, this book uses hands-on instruction to teach today’s most compelling, leading-edge computing technologies and programming in Python–one of the world’s most popular and fastest-growing languages. Please read the Table of Contents diagram inside the front cover and the Preface for more details.

In the context of 500+, real-world examples ranging from individual snippets to 40 large scripts and full implementation case studies, you’ll use the interactive IPython interpreter with code in Jupyter Notebooks to quickly master the latest Python coding idioms. 

After covering Python Chapters 1—5 and a few key parts of Chapters 6—7, you’ll be able to handle significant portions of the hands-on introductory AI case studies in Chapters 11—16, which are loaded with cool, powerful, contemporary examples. 

These include natural language processing, data mining Twitter for sentiment analysis, cognitive computing with IBM Watson™, supervised machine learning with classification and regression, unsupervised machine learning with clustering, computer vision through deep learning and convolutional neural networks, deep learning with recurrent neural networks, big data with Hadoop, Spark™ and NoSQL databases, the Internet of Things and more. You’ll also work directly or indirectly with cloud-based services, including Twitter, Google Translate™, IBM Watson, Microsoft Azure, OpenMapQuest, PubNub and more.

About The Book:

Publisher ‏ : ‎ Pearson (March 22, 2019)

Edition: 1

Language ‏ : ‎ English

Pages ‏ : ‎ 640 

File : PDF, 14.59 MB


Free Download the Book: Python for Programmers: with Big Data and Artificial Intelligence Case Studies

#python #programmers #bigdata #artificialintelligence #datascience #programming #developer 

Python for Programmers: Big Data and Artificial Intelligence Case Studies (PDF Book for FREE Download)
Felix Kling

Felix Kling


Getting Started with Hadoop & Apache Spark

Getting Started with Hadoop & Apache Spark

1 - Installing Debian

In this video we are installing Debian which we will use as an operating system to run a Hadoop and Apache Spark pseudo cluster.
This video covers creating a Virtual Machine in Windows, Downloading & Installing Debian, and the absolute basics of working with Linux.

2 - Downloading Hadoop
Here we will download Hadoop to our newly configured Virtual Machine. We will extract it and check whether it just works out of the box.

3 - Configuring Hadoop
After downloading and installing Hadoop we are going to configure it. After all configurations are done, we will have a working pseudo cluster for HDFS.

4 - Configuring YARN
After configuring our HDFS, we now want to configure a resource manager (YARN) to manage our pseudo cluster. For this we will adjust quite a few configurations. 
You can download my config file via the following link:

5 - Interacting with HDFS
After making all the configurations we can finally fire up our Hadoop cluster and start interacting with it. We will learn how to interact with HDFS such as listing the content and uploading data to it.

6 - Installing & Configuring Spark
After we are done configuring our HDFS, it is now time to get a good computation engine. For this we will download and configure Apache Spark.

7 - Loading Data into Spark
Having a running Spark pseudo cluster, we now want to load data from HDFS into a Spark data frame

8 - Running SQL Queries in Spark
Let us learn how to run typical SQL queries in Apache Spark. This includes selecting columns, filtering rows, joining tables, and creating new columns from existing ones.

9 - Save Data from Spark to HDFS
In the last video of this series we will save our Spark data frame into a Parquet file on HDFS.

#hadoop #apachespark #bigdata

Getting Started with Hadoop & Apache Spark
Mahoro  Trisha

Mahoro Trisha


Anomaly Detection and Data Conditioning in Machine Learning

Mathematics of Big Data and Machine Learning

Cyber Network Data Processing; AI Data Architecture

This lecture started with anomaly detection and data conditioning in machine learning and later continued to explore the AI data architecture in data processing.

#bigdata #machinelearning #ai #Mathematics 

Anomaly Detection and Data Conditioning in Machine Learning
Samuel Tucker

Samuel Tucker


Arrow Datafusion: Apache Arrow DataFusion and Ballista Query Engines


DataFusion is an extensible query execution framework, written in Rust, that uses Apache Arrow as its in-memory format.

DataFusion supports both an SQL and a DataFrame API for building logical query plans as well as a query optimizer and execution engine capable of parallel execution against partitioned data sources (CSV and Parquet) using threads.

DataFusion also supports distributed query execution via the Ballista crate.

Use Cases

DataFusion is used to create modern, fast and efficient data pipelines, ETL processes, and database systems, which need the performance of Rust and Apache Arrow and want to provide their users the convenience of an SQL interface or a DataFrame API.

Why DataFusion?

  • High Performance: Leveraging Rust and Arrow's memory model, DataFusion achieves very high performance
  • Easy to Connect: Being part of the Apache Arrow ecosystem (Arrow, Parquet and Flight), DataFusion works well with the rest of the big data ecosystem
  • Easy to Embed: Allowing extension at almost any point in its design, DataFusion can be tailored for your specific usecase
  • High Quality: Extensively tested, both by itself and with the rest of the Arrow ecosystem, DataFusion can be used as the foundation for production systems.

Known Uses

Projects that adapt to or serve as plugins to DataFusion:

Here are some of the projects known to use DataFusion:

(if you know of another project, please submit a PR to add a link!)

Example Usage

Run a SQL query against data stored in a CSV:

use datafusion::prelude::*;
use datafusion::arrow::util::pretty::print_batches;
use datafusion::arrow::record_batch::RecordBatch;

async fn main() -> datafusion::error::Result<()> {
  // register the table
  let mut ctx = ExecutionContext::new();
  ctx.register_csv("example", "tests/example.csv", CsvReadOptions::new()).await?;

  // create a plan to run a SQL query
  let df = ctx.sql("SELECT a, MIN(b) FROM example GROUP BY a LIMIT 100").await?;

  // execute and print results;

Use the DataFrame API to process data stored in a CSV:

use datafusion::prelude::*;
use datafusion::arrow::util::pretty::print_batches;
use datafusion::arrow::record_batch::RecordBatch;

async fn main() -> datafusion::error::Result<()> {
  // create the dataframe
  let mut ctx = ExecutionContext::new();
  let df = ctx.read_csv("tests/example.csv", CsvReadOptions::new()).await?;

  let df = df.filter(col("a").lt_eq(col("b")))?
          .aggregate(vec![col("a")], vec![min(col("b"))])?;

  // execute and print results

Both of these examples will produce

| a | MIN(b) |
| 1 | 2      |

Using DataFusion as a library

DataFusion is published on, and is well documented on

To get started, add the following to your Cargo.toml file:

datafusion = "6.0.0"

Using DataFusion as a binary

DataFusion also includes a simple command-line interactive SQL utility. See the CLI reference for more information.


A quarterly roadmap will be published to give the DataFusion community visibility into the priorities of the projects contributors. This roadmap is not binding.

2022 Q1

DataFusion Core

  • Publish official Arrow2 branch
  • Implementation of memory manager (i.e. to enable spilling to disk as needed)


  • Inclusion in Db-Benchmark with all quries covered
  • All TPCH queries covered

Performance Improvements

  • Predicate evaluation
  • Improve multi-column comparisons (that can't be vectorized at the moment)
  • Null constant support

New Features

  • Read JSON as table
  • Simplify DDL with Datafusion-Cli
  • Add Decimal128 data type and the attendant features such as Arrow Kernel and UDF support
  • Add new experimental e-graph based optimizer


  • Begin work on design documents and plan / priorities for development

Extensions (datafusion-contrib)

  • Stable S3 support
  • Begin design discussions and prototyping of a stream provider

Beyond 2022 Q1

There is no clear timeline for the below, but community members have expressed interest in working on these topics.

DataFusion Core

  • Custom SQL support
  • Split DataFusion into multiple crates
  • Push based query execution and code generation


  • Evolve architecture so that it can be deployed in a multi-tenant cloud native environment
  • Ensure Ballista is scalable, elastic, and stable for production usage
  • Develop distributed ML capabilities



  •  SQL Parser
  •  SQL Query Planner
  •  Query Optimizer
  •  Constant folding
  •  Join Reordering
  •  Limit Pushdown
  •  Projection push down
  •  Predicate push down
  •  Type coercion
  •  Parallel query execution

SQL Support

  •  Projection
  •  Filter (WHERE)
  •  Filter post-aggregate (HAVING)
  •  Limit
  •  Aggregate
  •  Common math functions
  •  cast
  •  try_cast
  •  VALUES lists
  • Postgres compatible String functions
    •  ascii
    •  bit_length
    •  btrim
    •  char_length
    •  character_length
    •  chr
    •  concat
    •  concat_ws
    •  initcap
    •  left
    •  length
    •  lpad
    •  ltrim
    •  octet_length
    •  regexp_replace
    •  repeat
    •  replace
    •  reverse
    •  right
    •  rpad
    •  rtrim
    •  split_part
    •  starts_with
    •  strpos
    •  substr
    •  to_hex
    •  translate
    •  trim
  • Miscellaneous/Boolean functions
    •  nullif
  • Approximation functions
    •  approx_distinct
  • Common date/time functions
  • nested functions
    •  Array of columns
  •  Schema Queries
    •  information_schema.{tables, columns}
    •  information_schema other views
  •  Sorting
  •  Nested types
  •  Lists
  •  Subqueries
  •  Common table expressions
  •  Set Operations
    •  UNION ALL
    •  UNION
    •  EXCEPT
  •  Joins
    •  LEFT JOIN
    •  FULL JOIN
  •  Window
    •  Empty window
    •  Common window functions
    •  Window with PARTITION BY clause
    •  Window with ORDER BY clause
    •  Window with FILTER clause
    •  Window with custom WINDOW FRAME
    •  UDF and UDAF for window functions

Data Sources

  •  CSV
  •  Parquet primitive types
  •  Parquet nested types


DataFusion is designed to be extensible at all points. To that end, you can provide your own custom:

  •  User Defined Functions (UDFs)
  •  User Defined Aggregate Functions (UDAFs)
  •  User Defined Table Source (TableProvider) for tables
  •  User Defined Optimizer passes (plan rewrites)
  •  User Defined LogicalPlan nodes
  •  User Defined ExecutionPlan nodes

Rust Version Compatbility

This crate is tested with the latest stable version of Rust. We do not currently test against other, older versions of the Rust compiler.

Supported SQL

This library currently supports many SQL constructs, including

  • CREATE EXTERNAL TABLE X STORED AS PARQUET LOCATION '...'; to register a table's locations
  • SELECT ... FROM ... together with any expression
  • ALIAS to name an expression
  • CAST to change types, including e.g. Timestamp(Nanosecond, None)
  • Many mathematical unary and binary expressions such as +, /, sqrt, tan, >=.
  • WHERE to filter
  • GROUP BY together with one of the following aggregations: MIN, MAX, COUNT, SUM, AVG, CORR, VAR, COVAR, STDDEV (sample and population)
  • ORDER BY together with an expression and optional ASC or DESC and also optional NULLS FIRST or NULLS LAST

Supported Functions

DataFusion strives to implement a subset of the PostgreSQL SQL dialect where possible. We explicitly choose a single dialect to maximize interoperability with other tools and allow reuse of the PostgreSQL documents and tutorials as much as possible.

Currently, only a subset of the PostgreSQL dialect is implemented, and we will document any deviations.

Schema Metadata / Information Schema Support

DataFusion supports the showing metadata about the tables available. This information can be accessed using the views of the ISO SQL information_schema schema or the DataFusion specific SHOW TABLES and SHOW COLUMNS commands.

More information can be found in the Postgres docs).

To show tables available for use in DataFusion, use the SHOW TABLES command or the information_schema.tables view:

> show tables;
| table_catalog | table_schema       | table_name | table_type |
| datafusion    | public             | t          | BASE TABLE |
| datafusion    | information_schema | tables     | VIEW       |

> select * from information_schema.tables;

| table_catalog | table_schema       | table_name | table_type   |
| datafusion    | public             | t          | BASE TABLE   |
| datafusion    | information_schema | TABLES     | SYSTEM TABLE |

To show the schema of a table in DataFusion, use the SHOW COLUMNS command or the or information_schema.columns view:

> show columns from t;
| table_catalog | table_schema | table_name | column_name | data_type | is_nullable |
| datafusion    | public       | t          | a           | Int32     | NO          |
| datafusion    | public       | t          | b           | Utf8      | NO          |
| datafusion    | public       | t          | c           | Float32   | NO          |

>   select table_name, column_name, ordinal_position, is_nullable, data_type from information_schema.columns;
| table_name | column_name | ordinal_position | is_nullable | data_type |
| t          | a           | 0                | NO          | Int32     |
| t          | b           | 1                | NO          | Utf8      |
| t          | c           | 2                | NO          | Float32   |

Supported Data Types

DataFusion uses Arrow, and thus the Arrow type system, for query execution. The SQL types from sqlparser-rs are mapped to Arrow types according to the following table

SQL Data TypeArrow DataType
UUIDNot yet supported
CLOBNot yet supported
BINARYNot yet supported
VARBINARYNot yet supported
INTERVALNot yet supported
REGCLASSNot yet supported
TEXTNot yet supported
BYTEANot yet supported
CUSTOMNot yet supported
ARRAYNot yet supported


Please see Roadmap for information of where the project is headed.

Architecture Overview

There is no formal document describing DataFusion's architecture yet, but the following presentations offer a good overview of its different components and how they interact together.

  • (March 2021): The DataFusion architecture is described in Query Engine Design and the Rust-Based DataFusion in Apache Arrow: recording (DataFusion content starts ~ 15 minutes in) and slides
  • (February 2021): How DataFusion is used within the Ballista Project is described in *Ballista: Distributed Compute with Rust and Apache Arrow: recording

Developer's guide

Please see Developers Guide for information about developing DataFusion.

Download Details: 
Author: apache
Source Code: 
License: Apache-2.0

#python #rust #sql #bigdata #arrow #dataframe #datafusion #apache 

Arrow Datafusion: Apache Arrow DataFusion and Ballista Query Engines
Joseph  Norton

Joseph Norton


Scale Validation Frameworks to Handle Big Data with Spark and Dask

Large Scale Data Validation with Fugue


As data teams scale, data pipelines become increasingly interconnected and often share components. Though efficient for development, upstream changes can cause unintended consequences to downstream datasets. In this talk, we’ll show how data validation solves this and especially focus on how to scale current validation frameworks to handle big data with Spark and Dask.


Data validation is implementing checks to see if data is coming in (and being processed) as expected. Data teams apply data validation to preserve the integrity of existing data workflows. As data pipelines become interconnected, it becomes very easy for one pipeline’s changes to cause breaking changes to other data applications. In situations like this, data validation serves both as tests for the pipeline, and as a monitoring solution to capture malformed data from flowing through the system. Without these checks, data applications can produce inaccurate results without anyone being alerted.

While data validation frameworks are available, it is still hard to bring these solutions to big data. Most frameworks are built for pandas and are challenging to apply with distributed compute frameworks such as Spark and Dask, if at all possible. In this talk, we will cover the basics of data validation, but more importantly, we will also discuss how to apply it on a large dataset.

To do this, we will use Fugue, an abstraction layer that enables users to port pandas, Python, and SQL code to Spark and Dask. By combining Fugue with existing validation frameworks such as Pandera, we can port pandas-based validation code and apply it distributedly. For large scale data, there is also a unique use case to apply different validations on different partitions of data. This is currently not feasible with any single validation library. In this talk, we will show how validation by partition can be achieved by combining Fugue and validation frameworks such as Pandera.

#bigdata #spark #dask

Scale Validation Frameworks to Handle Big Data with Spark and Dask
Aida  Stamm

Aida Stamm


Mathematics of Big Data and Machine Learning

Mathematics of Big Data and Machine Learning

Signal Processing on Databases

The head and founder of the MIT Lincoln Laboratory Supercomputing Center, Dr. Jeremy Kepner, shares why students should be interested in learning about mathematics of big data and how it relates to machine learning and other data processing and analysis challenges.

#bigdata #machinelearning

Mathematics of Big Data and Machine Learning

Big Data in AWS

Today, Big Data expanded across different verticals of businesses all around the world. The practical application of Big Data is reaching industries, scientific fields. The road of transforming a conventional society to a digitalized one requires processing, storing, and analyzing data. Data is valuable today, and in a world driven by huge volumes, data incites notable challenges and complexities.

Big Data was a to-go field for data management when conventional methods became stale to store data and process it efficiently. The answers offered by Big Data in AWS came to bridge the gap of creating and interpreting data efficiently. The technologies and tools give numerous possibilities as well as hurdles for exploring data efficiently. Understanding the choice of the customer is the need of the hour. Appropriating data to conduct market research provides a competitive edge to organizations.

AWS includes many different cloud computing products and services. The highly successful Amazon unit provides servers, storage, networking, remote computing, email, mobile development, and security. Furthermore. AWS consists of two main products: EC2, S3, a storage system and Amazon’s virtual machine service. It is so significant and is present in the computing world that it’s now at least ten times the size of its nearest competitor Netflix and Instagram.

Big Data in AWS Solutions

AWS platform brings in a bunch of constructive solutions for analysts, developers, and marketers. Moreover, AWS extends significant development to handle Big Data. Before exploring the tools, it is necessary to explore notable vital data segments, which amps the platform to provide solutions. By the looks of it, these four segments aid in delivering cutting-edge solutions that only AWS is capable of offering.


Learn more about Big Data in AWS from our blog.


#bigdata #aws #big-data #data-science #tech 

Big Data in AWS
Khalil  Torphy

Khalil Torphy


Build Large-Scale Data Analytics and AI Pipeline Using RayDP

A large-scale end-to-end data analytics and AI pipeline usually involves data processing frameworks such as Apache Spark for massive data preprocessing, and ML/DL frameworks for distributed training on the preprocessed data. A conventional approach is to use two separate clusters and glue multiple jobs. Other solutions include running deep learning frameworks in an Apache Spark cluster, or use workflow orchestrators like Kubeflow to stitch distributed programs. All these options have their own limitations. We introduce Ray as a single substrate for distributed data processing and machine learning. We also introduce RayDP which allows you to start an Apache Spark job on Ray in your python program and utilize Ray’s in-memory object store to efficiently exchange data between Apache Spark and other libraries. We will demonstrate how this makes building an end-to-end data analytics and AI pipeline simpler and more efficient.

#AI #bigdata 

Build Large-Scale Data Analytics and AI Pipeline Using RayDP