Big Data with Apache Hadoop and Solr
Big Data with Apache Hadoop and Solr Online by Checkmate IT Tech offers a transformative journey, elevating your expertise and mastering essential skills. Position yourself for success in the dynamic field of Big Data by enrolling today. Unlock new career opportunities!
- 10+ Courses
- 30+ Projects
- 400 Hours
Big Data with Apache Hadoop and Solr Training is suitable for the following target audiences:
Data Analysts: Experts wishing to use Solr’s sophisticated search features and Hadoop’s distributed processing capacity to improve their data processing and querying skills.
Software Developers: Software developers want to use Hadoop and Solr to create scalable data processing pipelines and search-based applications.
System Administrators: IT specialists who set up, maintain, and improve Solr setups and Hadoop clusters for massive data processes.
Business intelligence specialists: These are analysts and BI specialists who are looking for solutions to help them make strategic decisions by analyzing and visualizing massive datasets.
IT consultants: Advisors who help companies use Big Data solutions to boost productivity and creativity.
Data Scientists: Data scientists are professionals who want to handle large-scale data preparation and search by integrating Hadoop and Solr into machine learning operations.
Big Data Engineer: Developing effective workflows for data processing and designing and overseeing Hadoop environments.
Data Scientist: Data scientists use Solr for indexing and Hadoop for data preparation when analyzing large datasets to find patterns and insights.
Search Engineer: The search engineer is responsible for refining Solr for quick and effective searching in data-driven applications.
Data Architect: Creating and managing scalable data structures using Solr for search functions and Hadoop for data storage.
ETL Developer: Creating extract, transform, and load (ETL) procedures in Hadoop settings to oversee workflows involving big data.
Business Intelligence Developer: utilizing Solr to offer search capabilities within BI platforms and Hadoop for data storage.
Cloud Data Engineer: Cloud data engineers are responsible for deploying and overseeing Hadoop-based Big Data solutions on cloud platforms, frequently incorporating Solr for sophisticated search.
These positions are in high demand across industries like technology, healthcare, finance, and e-commerce, which provide competitive pay and opportunities for advancement in the quickly growing Big Data space.
- What is Big Data? Characteristics and challenges
- Overview of Hadoop and its components
- Hadoop Distributed File System architecture(HDFS)
- Hands-on: Installing Hadoop, HDFS basic operations
- Concepts: Mappers, Reducers, Jobs, and Tasks
- WordCount example and breakdown
- Writing and executing MapReduce jobs (Java/Python)
- Hands-on: Create and run your first MapReduce job
- Introduction to Hive, Pig, HBase, Sqoop, and Flume
- Batch vs. Real-time processing
- Hadoop YARN architecture and resource management
- Hands-on: Data ingestion using Sqoop/Flume into HDFS
- Introduction to HiveQL
- Tables, partitions, buckets
- Data types, loading and querying structured data
- Hands-on: Writing and executing Hive queries
- What is Apache Solr and why use it?
- Solr architecture and core concepts (core, schema, collections)
- Inverted indexing, analyzers, and tokenization
- Hands-on: Installing Solr, setting up a basic core, indexing sample data
- Schema design: fields, types, dynamic fields
- Indexing structured/unstructured data
- Query syntax, faceting, filtering, sorting
- Hands-on: Index data from HDFS into Solr using MapReduce or connectors
- Solr-Hadoop integration approaches (Solr Cell, Solr MapReduce Indexer)
- Real-time search from HDFS data
- Use cases: Log analysis, document indexing
- Hands-on: Create an end-to-end data pipeline using Hadoop + Solr
- Design and implement a mini-project
- Final project
Note: Curriculum can be modified as per latest industry standards.
The goal of this course is to instruct students on the storage, processing, and search of large-scale data using Apache Hadoop and Apache Solr. It addresses the concepts of end-to-end data pipelines, such as ingestion, indexing, and search.
This course is particularly well-suited for technical professionals who are interested in search engines and Big Data technologies, including developers, data engineers, and system administrators.
A fundamental comprehension of Linux commands, databases, and programming (preferably Java or Python) is advised, although it is not mandatory.
Hadoop, HDFS, MapReduce, Hive, Sqoop, and Apache Solr will be utilized for search and indexing. Additionally, you will acquire the knowledge necessary to integrate Solr with Hadoop data.
Yes, the course includes weekly lab sessions, coding exercises, and a final capstone project to build a working Big Data search solution.
Theory, tools, and practical exercises are addressed in one module per week during the eight-week course.
Solr is employed to index and search Big Data that is stored in Hadoop. You will acquire the ability to develop Solr schemas, index documents, and construct faceted search interfaces.
You can enroll via our website or contact our support team directly via email or phone. We’ll guide you through the quick and easy registration process.
https://checkmateittech.com/
Email info@checkmateittech.com OR Call Us +1-347-4082054
Yes, a Certificate of Completion is granted upon completion of all modules, submission of the capstone project, and successful completion of the final assessment.
You will develop a miniature end-to-end pipeline that ingests data into Hadoop, processes it with MapReduce or Hive, and indexes it in Solr for search and analysis.
This course equips you with the necessary skills to assume positions such as Hadoop/Solr Administrator, Data Engineer, Big Data Developer, and Search Engineer.
We currently offer online sessions with flexible weekday/weekend batches. All sessions are recorded. You’ll have access to the recordings, along with support from instructors and peers in our learning portal.
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Job opportunities in USA and Canada
Big Data Engineer: Developing effective workflows for data processing and designing and overseeing Hadoop environments.
Data Scientist: Data scientists use Solr for indexing and Hadoop for data preparation when analyzing large datasets to find patterns and insights.
Search Engineer: The search engineer is responsible for refining Solr for quick and effective searching in data-driven applications.
Data Architect: Creating and managing scalable data structures using Solr for search functions and Hadoop for data storage.
ETL Developer: Creating extract, transform, and load (ETL) procedures in Hadoop settings to oversee workflows involving big data.
Business Intelligence Developer: utilizing Solr to offer search capabilities within BI platforms and Hadoop for data storage.
Cloud Data Engineer: Cloud data engineers are responsible for deploying and overseeing Hadoop-based Big Data solutions on cloud platforms, frequently incorporating Solr for sophisticated search.
These positions are in high demand across industries like technology, healthcare, finance, and e-commerce, which provide competitive pay and opportunities for advancement in the quickly growing Big Data space.
