Supercharge your analytics workflow with https://bityl.co/Qcuk Apache Druid's real-time capabilities and seamless Kafka integration. Learn about it in just 14 steps.
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We will locate a real dataset, ingest it to our cluster, connect it to a database, apply some queries and data transformations on it , save our result and show it via BI tool.
The document provides an overview of Apache Hive Essentials Second Edition, which aims to help readers process and gain insights from big data using essential Hive techniques. The book covers topics such as setting up Hive, data definition and manipulation, performance considerations, extensibility features, and how Hive interacts with other tools. It is intended for data analysts, developers, and users looking to use Hive for exploring and analyzing data in Hadoop.
This document outlines the planning and implementation of a Hadoop cluster using Cloudera to process big data. Key points:
- Three CentOS Linux machines will be configured into a Hadoop cluster managed by Cloudera to process large datasets.
- Cloudera offers a GUI for managing Hadoop jobs, making it easier for users to process data than alternative options like Condor.
- The cluster will allow for cost-effective scaling by adding additional nodes as data volumes increase, rather than requiring new hardware.
- Implementation was done in VMware Workstation, with the first node used to install Cloudera and configure the other two cloned nodes and Windows client.
This document provides an overview of big data fundamentals and considerations for setting up a big data practice. It discusses key big data concepts like the four V's of big data. It also outlines common big data questions around business context, architecture, skills, and presents sample reference architectures. The document recommends starting a big data practice by identifying use cases, gaining management commitment, and setting up a center of excellence. It provides an example use case of retail web log analysis and presents big data architecture patterns.
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This document provides an overview of HDInsight and Hadoop. It defines big data and Hadoop, describing HDInsight as Microsoft's implementation of Hadoop in the cloud. It outlines the Hadoop ecosystem including HDFS, MapReduce, YARN, Hive, Pig and Sqoop. It discusses advantages of using HDInsight in the cloud and provides information on working with HDInsight clusters, loading and querying data, and different approaches to big data solutions.
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ETL is the process of extracting data from one location, transforming it, and loading it into a different location, often for the purposes of collection and analysis. As Hadoop becomes a common technology for sophisticated analysis and transformation of petabytes of structured and unstructured data, the task of moving data in and out efficiently becomes more important and writing transformation jobs becomes more complicated. Talend provides a way to build and automate complex ETL jobs for migration, synchronization, or warehousing tasks. Using Talend's Hadoop capabilities allows users to easily move data between Hadoop and a number of external data locations using over 450 connectors. Also, Talend can simplify the creation of MapReduce transformations by offering a graphical interface to Hive, Pig, and HDFS. In this talk, Cédric Carbone will discuss how to use Talend to move large amounts of data in and out of Hadoop and easily perform transformation tasks in a scalable way.
This document discusses how Apache Hadoop provides a solution for enterprises facing challenges from the massive growth of data. It describes how Hadoop can integrate with existing enterprise data systems like data warehouses to form a modern data architecture. Specifically, Hadoop provides lower costs for data storage, optimization of data warehouse workloads by offloading ETL tasks, and new opportunities for analytics through schema-on-read and multi-use data processing. The document outlines the core capabilities of Hadoop and how it has expanded to meet enterprise requirements for data management, access, governance, integration and security.
Enough taking about Big data and Hadoop and let’s see how Hadoop works in action.
We will locate a real dataset, ingest it to our cluster, connect it to a database, apply some queries and data transformations on it , save our result and show it via BI tool.
The document provides an overview of Apache Hive Essentials Second Edition, which aims to help readers process and gain insights from big data using essential Hive techniques. The book covers topics such as setting up Hive, data definition and manipulation, performance considerations, extensibility features, and how Hive interacts with other tools. It is intended for data analysts, developers, and users looking to use Hive for exploring and analyzing data in Hadoop.
This document outlines the planning and implementation of a Hadoop cluster using Cloudera to process big data. Key points:
- Three CentOS Linux machines will be configured into a Hadoop cluster managed by Cloudera to process large datasets.
- Cloudera offers a GUI for managing Hadoop jobs, making it easier for users to process data than alternative options like Condor.
- The cluster will allow for cost-effective scaling by adding additional nodes as data volumes increase, rather than requiring new hardware.
- Implementation was done in VMware Workstation, with the first node used to install Cloudera and configure the other two cloned nodes and Windows client.
This document provides an overview of big data fundamentals and considerations for setting up a big data practice. It discusses key big data concepts like the four V's of big data. It also outlines common big data questions around business context, architecture, skills, and presents sample reference architectures. The document recommends starting a big data practice by identifying use cases, gaining management commitment, and setting up a center of excellence. It provides an example use case of retail web log analysis and presents big data architecture patterns.
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In dieser Session stellen wir anhand eines praktischen Szenarios vor, wie konkrete Aufgabenstellungen mit HDInsight in der Praxis gelöst werden können:
- Grundlagen von HDInsight für Windows Server und Windows Azure
- Mit Windows Azure HDInsight arbeiten
- MapReduce-Jobs mit Javascript und .NET Code implementieren
This document provides an overview of HDInsight and Hadoop. It defines big data and Hadoop, describing HDInsight as Microsoft's implementation of Hadoop in the cloud. It outlines the Hadoop ecosystem including HDFS, MapReduce, YARN, Hive, Pig and Sqoop. It discusses advantages of using HDInsight in the cloud and provides information on working with HDInsight clusters, loading and querying data, and different approaches to big data solutions.
The document discusses big data and Hadoop. It describes the three V's of big data - variety, volume, and velocity. It also discusses Hadoop components like HDFS, MapReduce, Pig, Hive, and YARN. Hadoop is a framework for storing and processing large datasets in a distributed computing environment. It allows for the ability to store and use all types of data at scale using commodity hardware.
The document provides an overview of Hadoop, including:
- What Hadoop is and its core modules like HDFS, YARN, and MapReduce.
- Reasons for using Hadoop like its ability to process large datasets faster across clusters and provide predictive analytics.
- When Hadoop should and should not be used, such as for real-time analytics versus large, diverse datasets.
- Options for deploying Hadoop including as a service on cloud platforms, on infrastructure as a service providers, or on-premise with different distributions.
- Components that make up the Hadoop ecosystem like Pig, Hive, HBase, and Mahout.
This document discusses using Azure HDInsight for big data applications. It provides an overview of HDInsight and describes how it can be used for various big data scenarios like modern data warehousing, advanced analytics, and IoT. It also discusses the architecture and components of HDInsight, how to create and manage HDInsight clusters, and how HDInsight integrates with other Azure services for big data and analytics workloads.
Cloudwatt wanted to develop a big data analytics offering using Apache Hadoop on OpenStack but needed a hardware and software solution. A proof of concept using Intel Distribution for Apache Hadoop software on Intel Xeon processors with Intel SSDs showed faster cluster provisioning within 2 minutes and improved performance over HDDs. This enabled Cloudwatt to expand its cloud computing offering to include big data analytics attracting new customers and revenue.
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Discover HDP 2.2: Even Faster SQL Queries with Apache Hive and Stinger.nextHortonworks
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This document discusses Hortonworks and its mission to enable modern data architectures through Apache Hadoop. It provides details on Hortonworks' commitment to open source development through Apache, engineering Hadoop for enterprise use, and integrating Hadoop with existing technologies. The document outlines Hortonworks' services and the Hortonworks Data Platform (HDP) for storage, processing, and management of data in Hadoop. It also discusses Hortonworks' contributions to Apache Hadoop and related projects as well as enhancing SQL capabilities and performance in Apache Hive.
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This document provides an introduction to big data, including its key characteristics of volume, velocity, and variety. It describes different types of big data technologies like Hadoop, MapReduce, HDFS, Hive, and Pig. Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of computers. MapReduce is a programming model used for processing large datasets in a distributed computing environment. HDFS provides a distributed file system for storing large datasets across clusters. Hive and Pig provide data querying and analysis capabilities for data stored in Hadoop clusters using SQL-like and scripting languages respectively.
SQL Server 2016 introduces new features for business intelligence and reporting. PolyBase allows querying data across SQL Server and Hadoop using T-SQL. Integration Services has improved support for AlwaysOn availability groups and incremental package deployment. Reporting Services adds HTML5 rendering, PowerPoint export, and the ability to pin report items to Power BI dashboards. Mobile Report Publisher enables developing and publishing mobile reports.
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Azure Big Data: “Got Data? Go Modern and Monetize”.
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FlexPod Select for Hadoop is a pre-validated solution from Cisco and NetApp that provides an enterprise-class architecture for deploying Apache Hadoop workloads at scale. The solution includes Cisco UCS servers and fabric interconnects for compute, NetApp storage arrays, and Cloudera's Distribution of Apache Hadoop for the software stack. It offers benefits like high performance, reliability, scalability, simplified management, and reduced risk for organizations running business-critical Hadoop workloads.
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Hortonworks provides an open source Apache Hadoop distribution called Hortonworks Data Platform (HDP). Their mission is to enable modern data architectures through delivering enterprise Apache Hadoop. They have over 300 employees and are headquartered in Palo Alto, CA. Hortonworks focuses on driving innovation through the open source Apache community process, integrating Hadoop with existing technologies, and engineering Hadoop for enterprise reliability and support.
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The cloud revolution is here, and businesses that fail to embrace cloud migration risk falling behind.
Partner with Nitor infotech to navigate the cloud journey and unlock unprecedented opportunities for growth and innovation.
Navigating the cloud migration journey can be a daunting task, but with the right partner, the process becomes seamless. Nitor infotech, a leading cloud services provider, offers comprehensive solutions to help businesses of all sizes make a successful transition to the cloud. Their team of cloud experts guides clients through every step, from assessment and planning to implementation and optimization. By leveraging the latest cloud technologies, Nitor infotech empowers organizations to unlock the full potential of the cloud, driving increased efficiency, scalability, and cost savings. Whether you're looking to migrate your entire infrastructure or just specific workloads, Nitor infotech's customized approach ensures a tailored solution that meets your unique business needs. With a proven track record of successful cloud migrations, Nitor infotech is the trusted partner to help you embark on your cloud journey with confidence.
Learn more about Cloud migration services - https://bit.ly/4aPP53w
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The blessing and curse of today's database market? So many choices! While relational databases still dominate the day-to-day business, a host of alternatives has evolved around very specific use cases: graph, document, NoSQL, hybrid (HTAP), column store, the list goes on. And the database tools market is teeming with activity as well. Register for this special Research Webcast to hear Dr. Robin Bloor share his early findings about the evolving database market. He'll be joined by Steve Sarsfield of HPE Vertica, and Robert Reeves of Datical in a roundtable discussion with Bloor Group CEO Eric Kavanagh. Send any questions to info@insideanalysis.com, or tweet with #DBSurvival.
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Hands-on with Apache Druid: Installation & Data Ingestion Steps
1. Big Data and Analytics | 19 Jun 2024 | 11 min
Hands-on with Apache Druid: Installation &
Data Ingestion Steps
Rushikesh Pawar
Trainee Software Engineer
Rushikesh Pawar is a Trainee Software Engineer at Nitor Infotech. He is a
passionate software engineer specializing in data engineering, ade... Read
More
Are you in search of a solution that offers high-performance, column-oriented, real-time
analytics? How about a data store that can handle large volumes of data and provide
lightning-fast insights? Well, Apache Druid can do it all for you. Before proceeding with
this blog, I strongly recommend that you read my previous blog about Apache Druid, to
get a complete overview about its features, architecture, and comparisons with other
open-source database management systems.
Done reading? Great!
Now in this blog, you will dive into the world of Apache Druid and explore the step-by-
step process of installing and setting up this cutting-edge technology. You will also delve
into the intricacies of data ingestion, understanding how to seamlessly bring data into
Apache Druid for data analysis.
By the end of this blog, you will have a fully functional Apache Druid cluster ready to
handle real-time analytical needs for your business.
Prerequisites before installation
Before we dive into the details, it’s important to ensure you have the necessary
prerequisites in place. Quickly grasp what you’ll need:
Java Development Environment: At the foundation, you’ll require a Java
Development Kit (JDK) version 8 or higher installed on your system. The JDK
provides essential tools for developing and testing Java applications.
Operating System Familiarity: A solid understanding of Linux or Unix-based
operating systems is crucial. These platforms often form the backbone of server
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2. environments, and being comfortable with their command-line interfaces will be
highly valuable.
Big Data Infrastructure: Familiarity with distributed file systems, particularly the
Apache Hadoop Distributed File System (HDFS), is important. HDFS is designed to
handle large datasets efficiently on commodity hardware, making it a key
component in advanced analytics applications.
Data Formats: A basic understanding of SQL and JSON data formats is required.
SQL is the standard language for managing data in relational database
management systems, while JSON is a popular format for data interchange,
especially in web applications.
Streaming Platform: A fundamental knowledge of Apache Kafka, a distributed
streaming platform, will also be beneficial. Kafka is widely used for real-time data
processing, so having some familiarity with it will be advantageous.
Got your basics ready? Awesome! You are now set to embark on this journey of
installation and data ingestion with Apache Druid and discover how it can revolutionize
your data analytics workflows.
Quick Note:
Deploying Apache Druid on a single server and connecting it to Kafka for real-time data
ingestion can be achieved by following a few steps.
Let’s explore these steps in the next section!
14 Steps to Deploy Apache Druid with Kafka for
Real-Time Data Ingestion
Step 1: Install Java
Ensure that Java is installed on your system as it is essential for running Apache Druid.
Step 2: Verification
Ensure that both Java and Python are installed.
Step 3: Get Apache Druid
Download the Apache Druid tar file from the official website.
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3. Step 4: Extract the downloaded file
Extract the contents of the downloaded tar file to a directory on your system.
Step 5: Set Environment Variables
Set the JAVA_HOME and DRUID_HOME environment variables in your Linux.bashrc file to
point to the Java and Druid installation directories, respectively.
Step 6: Start Druid
Initiate the Apache Druid service by executing the “start-micro-quickstart” command.
This command allocates 4 CPUs and 16 GB of RAM to Druid.
Once started, access the Druid web console by copying the provided link into your
browser.
Step 7: Load Data
In the Druid web console, navigate to the “load data” section and choose “start a new
streaming spec”.
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4. Step 8: Connect to Kafka (here data is consumed from Kafka)
Select Apache Kafka as the data source and then click on Connect data.
Step 9: Configuration
Specify the Bootstrap Servers and Kafka Topic details. Click “Apply” and then “Next” to
proceed.
Step 10: Data Parsing
Once the data starts loading, check the following details according to data format, which
in this case is JSON.
After disabling the “Parse Kafka metadata” option, click Apply to view the data in a
tabular format. Then click Next.
Step 11: Data transformation
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5. After clicking ‘Next’ a few times, you will reach the data transformation options.
In the data transformation phase, you can perform column transformations, wherein you
will add a new column named “temp_F”. To accomplish this, navigate to the “Add column
transform” option, where you’ll be prompted to input details such as the name of the
column.
Keep the default type as “expression” and proceed to write an expression that calculates
the values for the new column.
In this instance, we are converting Celsius to Fahrenheit. Once the expression is defined,
the new column will be seamlessly incorporated into the dataset.
Step 12: Data segmentation
Now, we need to select the data segmentation criteria to create the data segment.
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6. Step 13: Finalize and Submit
After navigating through several screens by clicking ‘Next’, click on the ‘Submit’ button.
Once data ingestion is complete, navigate to the “data source” tab in the Druid web
console to view details of the ingested data source.
Step 14: Data Exploration
Navigate to the “Query” tab in the Druid web console to explore and query the ingested
data.
That’s it!
By following the 14 steps above, you will successfully deploy Apache Druid with Kafka for
real-time data ingestion.
As a recap, here are a few important things to keep in mind when installing Druid:
Ensure Python and Java are installed.
Configure environment variables like DRUID_HOME and JAVA_HOME.
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7. Launch Druid with the correct command for your computational needs.
Choose partitioning and segmentation criteria based on your data volume and
velocity to avoid segment issues.
In a nutshell, Apache Druid is a powerful tool that helps businesses make better
decisions using real-time data. It’s fast, scalable, and flexible, making it ideal for tasks
like interactive analytics, operational monitoring, and personalized recommendations.
With its ability to handle both historical and real-time data, Apache Druid is
transforming how businesses use data to drive success.
Now, it’s time to unleash the power of Apache Druid and unlock the full potential of your
data analytics workflows. Feel free to reach out to Nitor Infotech with your thoughts
about this blog.
Till then, happy exploring!
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