{"id":25816,"date":"2026-06-08T11:01:48","date_gmt":"2026-06-08T11:01:48","guid":{"rendered":"https:\/\/www.kaashivinfotech.com\/blog\/?p=25816"},"modified":"2026-06-08T11:05:46","modified_gmt":"2026-06-08T11:05:46","slug":"top-30-big-data-interview-questions-and-answers","status":"publish","type":"post","link":"https:\/\/www.kaashivinfotech.com\/blog\/top-30-big-data-interview-questions-and-answers\/","title":{"rendered":"25 Big Data Interview Questions That Actually Get Asked (With Real-World Answers &#038; Tips)"},"content":{"rendered":"\r\n<div class=\"-mb-4 min-h-0 flex-1\">\r\n<div class=\"relative overflow-hidden h-full min-h-0 flex-1\" dir=\"ltr\">\r\n<div class=\"h-full w-full rounded-[inherit] [&amp;&gt;div]:!block [&amp;&gt;div]:!h-full\" data-radix-scroll-area-viewport=\"\">\r\n<div class=\"relative h-full w-full overscroll-none\">\r\n<div class=\"flex flex-col items-center px-[--chat-padding] [--chat-padding:theme(spacing.4)]\">\r\n<div class=\"mx-auto max-w-[800px] w-full\">\r\n<div class=\"bg-surface-primary relative flex w-full min-w-0 flex-1 flex-col overflow-hidden transition-all duration-300 gap-2 border-border-faint\">\r\n<div class=\"no-scrollbar relative flex w-full flex-1 flex-col overflow-x-auto transition-[max-height] duration-300\">\r\n<div class=\"min-w-0\">\r\n<div class=\"flex flex-col gap-3\">\r\n<div class=\"prose prose-pre:bg-transparent prose-pre:p-0 text-wrap break-words prose-base body-base\">\r\n<p><strong>Big Data Interview Questions<\/strong>\u00a0are not just theoretical puzzles \u2014 they test how you think, solve problems, and design systems at scale. If you&#8217;re preparing for a data engineering, analytics, or big data role, you\u2019re probably wondering:<\/p>\r\n<ul>\r\n<li>What kind of questions do interviewers really ask?<\/li>\r\n<li>How deep should you go into Hadoop, Spark, or SQL?<\/li>\r\n<li>Will they test coding, architecture, or concepts?<\/li>\r\n<\/ul>\r\n<p>Let\u2019s clear the confusion right away.<\/p>\r\n<p>This guide breaks down the\u00a0<strong>most commonly asked Big Data Interview Questions<\/strong>, explains what interviewers expect, and helps you answer confidently \u2014 whether you&#8217;re a fresher or an experienced data engineer.<\/p>\r\n<hr \/>\r\n<h2>\u2705 Key Highlights<\/h2>\r\n<ul>\r\n<li>25 real-world\u00a0<strong>Big Data Interview Questions<\/strong><\/li>\r\n<li>Covers Hadoop, Spark, Kafka, Hive, SQL, and system design<\/li>\r\n<li>Includes\u00a0<strong>Data Engineer Interview Questions<\/strong>\u00a0companies frequently ask<\/li>\r\n<li>Real-world use cases and sample answers<\/li>\r\n<li>Best practices that actually impress interviewers<\/li>\r\n<li>FAQ section at the end<\/li>\r\n<\/ul>\r\n<hr \/>\r\n<h1>Why Companies Care So Much About Big Data Skills<\/h1>\r\n<p>Before jumping into questions, understand this:<\/p>\r\n<p>The global big data market is projected to reach\u00a0<strong>$401 billion by 2028<\/strong>\u00a0(Source: Fortune Business Insights). Companies process petabytes of data daily \u2014 Netflix, Amazon, Uber, banks, healthcare companies \u2014 all rely on scalable data systems.<\/p>\r\n<p>When they interview you, they don\u2019t just test tools. They test whether you can:<\/p>\r\n<ul>\r\n<li>Design scalable pipelines<\/li>\r\n<li>Handle failures<\/li>\r\n<li>Optimize performance<\/li>\r\n<li>Make business-driven decisions<\/li>\r\n<\/ul>\r\n<p>Now let\u2019s get into the real stuff.<\/p>\r\n<hr \/>\r\n<h1><img decoding=\"async\" class=\"aligncenter\" src=\"https:\/\/daxg39y63pxwu.cloudfront.net\/images\/blog\/big-data-interview-questions-\/Big_Data_Interview_Questions_and_Answers.png\" alt=\"100+ Big Data Interview Questions and Answers 2025\" \/><\/h1>\r\n<h1>\ud83d\udd25 Top 25 Big Data Interview Questions (With Practical Insight)<\/h1>\r\n<h2>1. What is Big Data?<\/h2>\r\n<p>Big Data refers to extremely large and complex datasets that traditional systems cannot process efficiently.<\/p>\r\n<p>It is commonly described using the\u00a0<strong>5 Vs<\/strong>:<\/p>\r\n<ul>\r\n<li>Volume<\/li>\r\n<li>Velocity<\/li>\r\n<li>Variety<\/li>\r\n<li>Veracity<\/li>\r\n<li>Value<\/li>\r\n<\/ul>\r\n<p>\ud83d\udca1 Tip: Always explain with a real-world example \u2014 like how Amazon processes billions of customer interactions daily.<\/p>\r\n<hr \/>\r\n<h2>2. What is Hadoop and how does it work?<\/h2>\r\n<p>Hadoop is an open-source framework that enables distributed storage and processing of large datasets.<\/p>\r\n<p>It has two core components:<\/p>\r\n<ul>\r\n<li><strong>HDFS<\/strong>\u00a0(storage layer)<\/li>\r\n<li><strong>MapReduce<\/strong>\u00a0(processing layer)<\/li>\r\n<\/ul>\r\n<p>Explain how data splits into blocks and distributes across nodes. Mention fault tolerance.<\/p>\r\n<p>Reference:\u00a0<a class=\"text-interactive-link\" href=\"http:\/\/Hadoop Apache\" target=\"_blank\" rel=\"noopener\">Hadoop Apache<\/a><\/p>\r\n<hr \/>\r\n<h2><img decoding=\"async\" class=\"aligncenter\" src=\"https:\/\/intellipaat.com\/blog\/wp-content\/uploads\/2026\/05\/hadoop-vs-spark.webp\" alt=\"Hadoop vs Spark: Major Differences Explained - Intellipaat\" \/><\/h2>\r\n<h2>3. What is the difference between Hadoop and Spark?<\/h2>\r\n<p>This is one of the most common\u00a0<strong>Data Engineer Interview Questions<\/strong>.<\/p>\r\n<div class=\"overflow-x-auto\">\r\n<table class=\"min-w-full\">\r\n<thead>\r\n<tr>\r\n<th class=\"whitespace-nowrap px-3 py-2\">Hadoop<\/th>\r\n<th class=\"whitespace-nowrap px-3 py-2\">Spark<\/th>\r\n<\/tr>\r\n<\/thead>\r\n<tbody>\r\n<tr>\r\n<td class=\"px-3 py-2\">Disk-based processing<\/td>\r\n<td class=\"px-3 py-2\">In-memory processing<\/td>\r\n<\/tr>\r\n<tr>\r\n<td class=\"px-3 py-2\">Slower<\/td>\r\n<td class=\"px-3 py-2\">Up to 100x faster (Apache Spark claims this for in-memory workloads)<\/td>\r\n<\/tr>\r\n<tr>\r\n<td class=\"px-3 py-2\">Batch processing<\/td>\r\n<td class=\"px-3 py-2\">Batch + Streaming<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/div>\r\n<p>\ud83d\udca1 Real Insight: Most companies now prefer Spark over MapReduce for performance reasons.<\/p>\r\n<hr \/>\r\n<h2>4. What is HDFS?<\/h2>\r\n<p>HDFS (Hadoop Distributed File System) stores data across multiple machines.<\/p>\r\n<p>Key features:<\/p>\r\n<ul>\r\n<li>Fault tolerance<\/li>\r\n<li>High throughput<\/li>\r\n<li>Data replication (default factor = 3)<\/li>\r\n<\/ul>\r\n<p>Explain NameNode and DataNode roles.<\/p>\r\n<hr \/>\r\n<h2>5. What happens when a DataNode fails?<\/h2>\r\n<p>This tests real-world understanding.<\/p>\r\n<p>Answer:<\/p>\r\n<ul>\r\n<li>NameNode detects failure<\/li>\r\n<li>Replicates blocks to maintain replication factor<\/li>\r\n<li>Ensures no data loss<\/li>\r\n<\/ul>\r\n<p>Companies love this question because it checks system-level thinking.<\/p>\r\n<hr \/>\r\n<h2>6. What is Spark Architecture?<\/h2>\r\n<p>Spark consists of:<\/p>\r\n<ul>\r\n<li>Driver Program<\/li>\r\n<li>Cluster Manager<\/li>\r\n<li>Executors<\/li>\r\n<li>Tasks<\/li>\r\n<\/ul>\r\n<p>Explain lazy evaluation and DAG (Directed Acyclic Graph).<\/p>\r\n<hr \/>\r\n<h2>7. What are RDDs?<\/h2>\r\n<p>RDD (Resilient Distributed Dataset) is a fundamental Spark data structure.<\/p>\r\n<p>Characteristics:<\/p>\r\n<ul>\r\n<li>Immutable<\/li>\r\n<li>Distributed<\/li>\r\n<li>Fault-tolerant<\/li>\r\n<\/ul>\r\n<p>Mention transformations vs actions.<\/p>\r\n<hr \/>\r\n<h2><img decoding=\"async\" class=\"aligncenter\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2020\/11\/RDDs-Dataframes-and-Datasets.jpg\" alt=\"RDDs vs Dataframes vs Datasets : Learn the Differences\" \/><\/h2>\r\n<h2>8. What is the difference between RDD, DataFrame, and Dataset?<\/h2>\r\n<p>Modern interviews focus here.<\/p>\r\n<ul>\r\n<li>RDD \u2192 Low-level control<\/li>\r\n<li>DataFrame \u2192 Structured data with schema<\/li>\r\n<li>Dataset \u2192 Type-safe, optimized<\/li>\r\n<\/ul>\r\n<p>Best Practice: Say companies prefer DataFrames due to Catalyst Optimizer.<\/p>\r\n<hr \/>\r\n<h2>9. What is data partitioning?<\/h2>\r\n<p>Partitioning divides data across nodes.<\/p>\r\n<p>Why it matters?<\/p>\r\n<ul>\r\n<li>Improves performance<\/li>\r\n<li>Enables parallelism<\/li>\r\n<li>Reduces shuffle<\/li>\r\n<\/ul>\r\n<p>Real-world example:<br \/>If you partition sales data by country, queries run faster for country-specific reports.<\/p>\r\n<hr \/>\r\n<h2>10. What is data skew?<\/h2>\r\n<p>Data skew happens when data distributes unevenly across partitions.<\/p>\r\n<p>Impact:<\/p>\r\n<ul>\r\n<li>Some nodes overloaded<\/li>\r\n<li>Slow performance<\/li>\r\n<\/ul>\r\n<p>Solution:<\/p>\r\n<ul>\r\n<li>Salting keys<\/li>\r\n<li>Repartitioning<\/li>\r\n<li>Broadcast joins<\/li>\r\n<\/ul>\r\n<p>This is a high-level\u00a0<strong>Big Data Interview Question<\/strong>\u00a0for experienced roles.<\/p>\r\n<hr \/>\r\n<h2>11. What is Kafka?<\/h2>\r\n<p>Apache Kafka is a distributed event streaming platform.<\/p>\r\n<p>Used for:<\/p>\r\n<ul>\r\n<li>Real-time pipelines<\/li>\r\n<li>Messaging systems<\/li>\r\n<li>Log aggregation<\/li>\r\n<\/ul>\r\n<p>Official Site: <a class=\"text-interactive-link\" href=\"https:\/\/kafka.apache.org\/\" target=\"_blank\" rel=\"noopener noreferrer\">kafka.apache<\/a><\/p>\r\n<hr \/>\r\n<h2>12. What is the difference between batch and stream processing?<\/h2>\r\n<p>Batch \u2192 Process stored data<br \/>Stream \u2192 Process real-time data<\/p>\r\n<p>Examples:<\/p>\r\n<ul>\r\n<li>Payroll system (batch)<\/li>\r\n<li>Fraud detection (stream)<\/li>\r\n<\/ul>\r\n<hr \/>\r\n<h2>13. How would you design a data pipeline?<\/h2>\r\n<p>This is a classic\u00a0<strong>Data Engineer Interview Question<\/strong>.<\/p>\r\n<p>Structure your answer:<\/p>\r\n<ol>\r\n<li>Data ingestion (Kafka)<\/li>\r\n<li>Processing (Spark)<\/li>\r\n<li>Storage (S3\/HDFS)<\/li>\r\n<li>Data warehouse (Snowflake\/Redshift)<\/li>\r\n<li>Monitoring (Airflow)<\/li>\r\n<\/ol>\r\n<p>Talk about scalability and fault tolerance.<\/p>\r\n<hr \/>\r\n<h2>14. What is Hive?<\/h2>\r\n<p>Hive is a data warehouse built on Hadoop.<\/p>\r\n<p>Uses SQL-like language called HiveQL.<\/p>\r\n<p>Great for batch analytics.<\/p>\r\n<hr \/>\r\n<h2>15. What is partitioning vs bucketing in Hive?<\/h2>\r\n<p>Partitioning \u2192 Divide data by column values<br \/>Bucketing \u2192 Distribute data into fixed buckets using hash function<\/p>\r\n<p>Use case:<br \/>Large datasets with frequent joins.<\/p>\r\n<hr \/>\r\n<h2>16. What is data replication?<\/h2>\r\n<p>Replication creates multiple copies of data blocks.<\/p>\r\n<p>Default replication factor in HDFS = 3.<\/p>\r\n<p>Improves:<\/p>\r\n<ul>\r\n<li>Fault tolerance<\/li>\r\n<li>Availability<\/li>\r\n<\/ul>\r\n<hr \/>\r\n<h2>17. What is CAP theorem?<\/h2>\r\n<p>CAP stands for:<\/p>\r\n<ul>\r\n<li>Consistency<\/li>\r\n<li>Availability<\/li>\r\n<li>Partition tolerance<\/li>\r\n<\/ul>\r\n<p>You can only guarantee two out of three.<\/p>\r\n<p>Use example:<br \/>Banking system \u2192 Consistency prioritized<br \/>Social media \u2192 Availability prioritized<\/p>\r\n<hr \/>\r\n<h2>18. What is schema-on-read vs schema-on-write?<\/h2>\r\n<p>Schema-on-write \u2192 Traditional databases<br \/>Schema-on-read \u2192 Hadoop systems<\/p>\r\n<p>Modern data lakes use schema-on-read.<\/p>\r\n<hr \/>\r\n<h2>19. What are common big data file formats?<\/h2>\r\n<ul>\r\n<li>Parquet<\/li>\r\n<li>ORC<\/li>\r\n<li>Avro<\/li>\r\n<li>JSON<\/li>\r\n<\/ul>\r\n<p>Best Practice:<br \/>Use Parquet for analytics (columnar format, compression).<\/p>\r\n<hr \/>\r\n<h2>20. What is Airflow?<\/h2>\r\n<p>Apache Airflow schedules and monitors workflows.<\/p>\r\n<p>Used for:<\/p>\r\n<ul>\r\n<li>ETL orchestration<\/li>\r\n<li>Data pipeline automation<\/li>\r\n<\/ul>\r\n<hr \/>\r\n<h2>21. How do you handle late-arriving data?<\/h2>\r\n<p>Answer with:<\/p>\r\n<ul>\r\n<li>Watermarks (Spark Streaming)<\/li>\r\n<li>Reprocessing pipelines<\/li>\r\n<li>Delta Lake merges<\/li>\r\n<\/ul>\r\n<p>Shows production experience.<\/p>\r\n<hr \/>\r\n<h2>22. What is ETL vs ELT?<\/h2>\r\n<p>ETL \u2192 Transform before loading<br \/>ELT \u2192 Load first, transform later<\/p>\r\n<p>Cloud data warehouses favor ELT.<\/p>\r\n<hr \/>\r\n<h2>23. What is data lake vs data warehouse?<\/h2>\r\n<p>Data Lake:<\/p>\r\n<ul>\r\n<li>Raw data<\/li>\r\n<li>Schema-on-read<\/li>\r\n<\/ul>\r\n<p>Data Warehouse:<\/p>\r\n<ul>\r\n<li>Structured<\/li>\r\n<li>Optimized for BI<\/li>\r\n<\/ul>\r\n<hr \/>\r\n<h2>24. How do you optimize Spark jobs?<\/h2>\r\n<p>Best practices:<\/p>\r\n<ul>\r\n<li>Avoid wide transformations<\/li>\r\n<li>Use broadcast joins<\/li>\r\n<li>Cache wisely<\/li>\r\n<li>Tune shuffle partitions<\/li>\r\n<li>Use columnar formats<\/li>\r\n<\/ul>\r\n<p>Explain why \u2014 less shuffle means less network overhead.<\/p>\r\n<hr \/>\r\n<h2>25. How do you ensure data quality?<\/h2>\r\n<p>Answer structure:<\/p>\r\n<ul>\r\n<li>Validation rules<\/li>\r\n<li>Monitoring tools<\/li>\r\n<li>Logging<\/li>\r\n<li>Automated alerts<\/li>\r\n<\/ul>\r\n<p>Real companies use tools like Great Expectations.<\/p>\r\n<hr \/>\r\n<h1>\ud83e\udde0 Real Interview Strategy That Works<\/h1>\r\n<p>Here\u2019s what many candidates get wrong:<\/p>\r\n<p>They memorize answers.<\/p>\r\n<p>Don\u2019t do that.<\/p>\r\n<p>Instead:<\/p>\r\n<ul>\r\n<li>Understand concepts deeply<\/li>\r\n<li>Practice explaining simply<\/li>\r\n<li>Relate answers to business impact<\/li>\r\n<\/ul>\r\n<p>Interviewers hire problem-solvers, not Wikipedia.<\/p>\r\n<hr \/>\r\n<p>&nbsp;<\/p>\r\n<ul>\r\n<li>Check out our guide on\u00a0<a href=\"https:\/\/course.kaashivinfotech.com\/data-science-course-in-chennai\"><em>How to Become a Data Engineer<\/em><\/a><\/li>\r\n<li>Read our article on\u00a0<a href=\"https:\/\/kaashiv.com\/internship\/sql-interview-questions\" target=\"_blank\" rel=\"noopener\"><em>Top SQL Interview Questions for Data Roles<\/em><\/a><\/li>\r\n<li>Explore our\u00a0<a href=\"https:\/\/course.kaashivinfotech.com\/data-science-course-in-chennai\"><em>Data Engineering Roadmap<\/em><\/a><\/li>\r\n<\/ul>\r\n<hr \/>\r\n<h1>\u2753 FAQ \u2013 Big Data Interview Questions<\/h1>\r\n<h3>1. Are Big Data Interview Questions difficult?<\/h3>\r\n<p>They can be. Entry-level roles focus on basics. Senior roles test architecture and optimization.<\/p>\r\n<hr \/>\r\n<h3>2. How should you prepare for Data Engineer Interview Questions?<\/h3>\r\n<ul>\r\n<li>Practice system design<\/li>\r\n<li>Build real projects<\/li>\r\n<li>Use cloud platforms (AWS, Azure, GCP)<\/li>\r\n<li>Revise SQL thoroughly<\/li>\r\n<\/ul>\r\n<hr \/>\r\n<h3>3. Do companies still ask Hadoop questions?<\/h3>\r\n<p>Yes, but focus is shifting toward Spark and cloud data tools.<\/p>\r\n<hr \/>\r\n<h3>4. How many Big Data Interview Questions should you prepare?<\/h3>\r\n<p>Prepare at least 30\u201340 solid conceptual and scenario-based questions.<\/p>\r\n<hr \/>\r\n<h3>5. Do I need coding skills for big data interviews?<\/h3>\r\n<p>Yes. Most roles test:<\/p>\r\n<ul>\r\n<li>SQL<\/li>\r\n<li>Python<\/li>\r\n<li>Spark<\/li>\r\n<\/ul>\r\n<hr \/>\r\n<h1>Final Thoughts<\/h1>\r\n<p>Preparing for\u00a0<strong>Big Data Interview Questions<\/strong>\u00a0can feel overwhelming. The tools are many. The concepts are deep. The expectations are high.<\/p>\r\n<p>But here\u2019s the truth:<\/p>\r\n<p>If you understand how data flows through a system \u2014 from ingestion to analytics \u2014 you already stand ahead of 70% of candidates.<\/p>\r\n<p>Focus on fundamentals. Build one solid project. Practice explaining your thinking clearly.<\/p>\r\n<p>You don\u2019t need to know everything.<\/p>\r\n<p>You need to know how to solve problems at scale.<\/p>\r\n<p>And that\u2019s exactly what interviewers look for. \ud83d\ude80<\/p>\r\n\r\n\r\n\r\n<div class=\"relative flex flex-col items-center pb-6 px-4\">\r\n<div class=\"flex w-full flex-col gap-3 max-w-screen-xl\">\r\n<div class=\"relative order-2 w-full md:order-1 mx-auto max-w-[800px]\">\r\n<div class=\"bg-surface-primary relative flex flex-col rounded-[0.9rem] md:items-center md:gap-2\">\r\n<div class=\"bg-surface-secondary relative mx-auto min-h-0 w-full flex-none rounded-[14px] border md:mx-4 border-border-faint\"><form class=\"flex w-full flex-col items-start justify-center p-2\">\r\n<div class=\"flex w-full flex-col justify-between gap-5 md:gap-2\">\u00a0<\/div>\r\n<\/form><\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Looking for the best Big Data interview preparation? This guide covers the Top 30 Big Data Interview Questions and Answers, including Hadoop, Spark, Hive, HDFS, MapReduce, and real-world scenarios to help freshers and experienced professionals succeed in 2026.<\/p>\n","protected":false},"author":42,"featured_media":25819,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[724],"tags":[14856,14870,14855,14862,14868,12470,14847,12475,14852,14859,14854,14850,11540,12468,14866,14845,14865,14867,14869,14858,14864,14851,14848,14846,14860,14861,14853,14863,14857],"class_list":["post-25816","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-interview-questions","tag-advanced-big-data-interview-questions","tag-analytics-interview-questions","tag-apache-spark-interview-questions","tag-big-data-analytics-interview-questions","tag-big-data-certification","tag-big-data-concepts","tag-big-data-engineer-interview-questions","tag-big-data-interview-questions","tag-big-data-interview-questions-and-answers","tag-big-data-interview-questions-for-experienced","tag-big-data-interview-questions-for-freshers","tag-big-data-jobs","tag-big-data-technology","tag-big-data-tutorial","tag-cloud-data-engineering","tag-data-engineer-interview-questions","tag-data-processing","tag-data-science-interview-questions","tag-distributed-computing","tag-etl-interview-questions","tag-hadoop-developer-interview-questions","tag-hadoop-interview-questions","tag-hdfs-interview-questions","tag-hive-interview-questions","tag-mapreduce-interview-questions","tag-real-time-big-data-interview-questions","tag-spark-sql-interview-questions","tag-tech-interview-preparation","tag-top-30-big-data-interview-questions"],"_links":{"self":[{"href":"https:\/\/www.kaashivinfotech.com\/blog\/wp-json\/wp\/v2\/posts\/25816","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.kaashivinfotech.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.kaashivinfotech.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.kaashivinfotech.com\/blog\/wp-json\/wp\/v2\/users\/42"}],"replies":[{"embeddable":true,"href":"https:\/\/www.kaashivinfotech.com\/blog\/wp-json\/wp\/v2\/comments?post=25816"}],"version-history":[{"count":0,"href":"https:\/\/www.kaashivinfotech.com\/blog\/wp-json\/wp\/v2\/posts\/25816\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.kaashivinfotech.com\/blog\/wp-json\/wp\/v2\/media\/25819"}],"wp:attachment":[{"href":"https:\/\/www.kaashivinfotech.com\/blog\/wp-json\/wp\/v2\/media?parent=25816"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.kaashivinfotech.com\/blog\/wp-json\/wp\/v2\/categories?post=25816"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.kaashivinfotech.com\/blog\/wp-json\/wp\/v2\/tags?post=25816"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}