{"id":4684,"date":"2025-10-27T04:58:36","date_gmt":"2025-10-27T04:58:36","guid":{"rendered":"https:\/\/omxwebsites.com\/gammatica\/?page_id=4684"},"modified":"2025-11-07T05:42:59","modified_gmt":"2025-11-07T05:42:59","slug":"big-data-engineering-basics","status":"publish","type":"page","link":"https:\/\/omxwebsites.com\/gammatica\/big-data-engineering-basics\/","title":{"rendered":"Big Data Engineering- Basics"},"content":{"rendered":"\t\t
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Big Data- Basics<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Learning Format<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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Total training duration<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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80-90 hrs (2 months)<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Syllabus<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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Certification<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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Big Data Engineering \u2013 Basics<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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\n\t\t\t\t\t\t\t\t\tBig Data Engineering is the process of designing, building, and managing systems that collect, store, and analyze large volumes of data. In today\u2019s digital world, organizations generate massive amounts of data every second\u2014from social media, sensors, transactions, and applications. Big Data Engineers create the pipelines and infrastructure that make it possible to transform this raw data into meaningful insights.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
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Syllabus Summary<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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Big Data Foundations-<\/strong><\/p>

  • What is Big Data? Batch vs Streaming processing<\/li>
  • Hadoop ecosystem overview (HDFS, YARN, Hive, Spark)<\/li>
  • Why Spark over MapReduce?<\/li>
  • Industry use cases of Big Data<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t
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    SQL Basics (Part 1)-<\/strong><\/p>

    • RDBMS concepts refresher<\/li>
    • SQL DDL, DML (CREATE, INSERT, UPDATE, DELETE)-<\/li>
    • SELECT statements & WHERE filters<\/li>
    • ORDER BY, LIMIT usage<\/li>
    • Hands-on: Run SQL queries on sample datasets<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t
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      SQL Basics (Part 2)-<\/strong><\/p>

      • Aggregations:<\/strong> SUM, COUNT, AVG, MIN, MAX<\/li>
      • GROUP BY, HAVING<\/li>
      • Combining filters with aggregations<\/li>
      • Case Study:<\/strong> Retail\/Banking dataset analysis<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t
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        SQL Joins<\/strong><\/p>

        • INNER, LEFT, RIGHT, FULL Joins<\/li>
        • Hands-on:<\/strong> Joining Orders + Customers dataset<\/li>
        • Joins with multiple table<\/li>
        • Business case study queries<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t
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          Introduction to Hive<\/strong><\/p>

          • Hive architecture & components (Metastore, HDFS)<\/li>
          • Difference between SQL & HiveQL<\/li>
          • Creating & loading Hive tables<\/li>
          • Hands-on:<\/strong> Query structured data in Hive<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t
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            Hive Queries & Integrations<\/strong><\/p>

            • Hive DDL\/DML commands<\/li>
            • Partitioning & Bucketing basics<\/li>
            • Performance considerations in Hive<\/li>
            • Mini Project:<\/strong> Sales dataset analysis in Hive<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t
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              Python for Data Engineering<\/strong><\/p>

              • Python syntax, loops, functions, file handling<\/li>
              • Pandas & NumPy basics for analysis<\/li>
              • Hands-on:<\/strong> Cleaning CSV\/JSON with Pandas<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t
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                Intro to PySpark & Wrap-Up<\/strong><\/p>

                • \u00a0What is PySpark? Scaling Python to Big Data<\/li>
                • Creating Spark DataFrames<\/li>
                • Simple transformations (select, filter, withColumn)<\/li>
                • Module recap & review<\/li>
                • Mock Interview 1 (SQL + Hive + Python basics)<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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                  Course Summary<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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                  \n\t\t\t\t\t\tTech & Non-Tech Working professional, Freshers, Graduate from any domain.\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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                  \n\t\t\t\t\t\tGet your queries solved with daily dedicated doubts solving sessions.\n\n\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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                  \n\t\t\t\t\t\tExperts and trainer for top-tech companies.\n\n\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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                  \n\t\t\t\t\t\t10+ ISO Globally recognized certified\n\n\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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                  \n\t\t\t\t\t\t100% Live Learning with experienced instructors and hands-on sessions.\n\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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                  \n\t\t\t\t\t\tGet practical experience with real-world projects for a career in analytics.\n\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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                  \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

                  Big Data- Basics Learning Format Online mode Total training duration 80-90 hrs (2 months) Syllabus 8 weeks Certification Yes Big Data Engineering \u2013 Basics Big Data Engineering is the process of designing, building, and managing systems that collect, store, and analyze large volumes of data. In today\u2019s digital world, organizations generate massive amounts of data every second\u2014from social media, sensors, transactions, and applications. Big Data Engineers create the pipelines and infrastructure that make it possible to transform this raw data into meaningful insights. Enroll Now Syllabus Summary Week 1 Big Data Foundations- What is Big Data? Batch vs Streaming processing Hadoop ecosystem overview (HDFS, YARN, Hive, Spark) Why Spark over MapReduce? Industry use cases of Big Data Week 2 SQL Basics (Part 1)- RDBMS concepts refresher SQL DDL, DML (CREATE, INSERT, UPDATE, DELETE)- SELECT statements & WHERE filters ORDER BY, LIMIT usage Hands-on: Run SQL queries on sample datasets Week 3 SQL Basics (Part 2)- Aggregations: SUM, COUNT, AVG, MIN, MAX GROUP BY, HAVING Combining filters with aggregations Case Study: Retail\/Banking dataset analysis Week 4 SQL Joins INNER, LEFT, RIGHT, FULL Joins Hands-on: Joining Orders + Customers dataset Joins with multiple table Business case study queries Week 5 Introduction to Hive Hive architecture & components (Metastore, HDFS) Difference between SQL & HiveQL Creating & loading Hive tables Hands-on: Query structured data in Hive Week 6 Hive Queries & Integrations Hive DDL\/DML commands Partitioning & Bucketing basics Performance considerations in Hive Mini Project: Sales dataset analysis in Hive Week 7 Python for Data Engineering Python syntax, loops, functions, file handling Pandas & NumPy basics for analysis Hands-on: Cleaning CSV\/JSON with Pandas Week 8 Intro to PySpark & Wrap-Up \u00a0What is PySpark? Scaling Python to Big Data Creating Spark DataFrames Simple transformations (select, filter, withColumn) Module recap & review Mock Interview 1 (SQL + Hive + Python basics) Big Data Foundations- What is Big Data? Batch vs Streaming processing Hadoop ecosystem overview (HDFS, YARN, Hive, Spark) Why Spark over MapReduce? Industry use cases of Big Data SQL Basics (Part 1)- RDBMS concepts refresher SQL DDL, DML (CREATE, INSERT, UPDATE, DELETE)- SELECT statements & WHERE filters ORDER BY, LIMIT usage Hands-on: Run SQL queries on sample datasets SQL Basics (Part 2)- Aggregations: SUM, COUNT, AVG, MIN, MAX GROUP BY, HAVING Combining filters with aggregations Case Study: Retail\/Banking dataset analysis SQL Joins INNER, LEFT, RIGHT, FULL Joins Hands-on: Joining Orders + Customers dataset Joins with multiple table Business case study queries Introduction to Hive Hive architecture & components (Metastore, HDFS) Difference between SQL & HiveQL Creating & loading Hive tables Hands-on: Query structured data in Hive Hive Queries & Integrations Hive DDL\/DML commands Partitioning & Bucketing basics Performance considerations in Hive Mini Project: Sales dataset analysis in Hive Python for Data Engineering Python syntax, loops, functions, file handling Pandas & NumPy basics for analysis Hands-on: Cleaning CSV\/JSON with Pandas Intro to PySpark & Wrap-Up \u00a0What is PySpark? Scaling Python to Big Data Creating Spark DataFrames Simple transformations (select, filter, withColumn) Module recap & review Mock Interview 1 (SQL + Hive + Python basics) Course Summary Eligibility Tech & Non-Tech Working professional, Freshers, Graduate from any domain. Live Doubt Solving Get your queries solved with daily dedicated doubts solving sessions. Instructor Experts and trainer for top-tech companies. Certification 10+ ISO Globally recognized certified Mode of Learning 100% Live Learning with experienced instructors and hands-on sessions. Real time projects Get practical experience with real-world projects for a career in analytics. Certification<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"elementor_canvas","meta":{"site-sidebar-layout":"no-sidebar","site-content-layout":"","ast-site-content-layout":"full-width-container","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-4684","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/omxwebsites.com\/gammatica\/wp-json\/wp\/v2\/pages\/4684","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/omxwebsites.com\/gammatica\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/omxwebsites.com\/gammatica\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/omxwebsites.com\/gammatica\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/omxwebsites.com\/gammatica\/wp-json\/wp\/v2\/comments?post=4684"}],"version-history":[{"count":28,"href":"https:\/\/omxwebsites.com\/gammatica\/wp-json\/wp\/v2\/pages\/4684\/revisions"}],"predecessor-version":[{"id":7796,"href":"https:\/\/omxwebsites.com\/gammatica\/wp-json\/wp\/v2\/pages\/4684\/revisions\/7796"}],"wp:attachment":[{"href":"https:\/\/omxwebsites.com\/gammatica\/wp-json\/wp\/v2\/media?parent=4684"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}