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Questions and Answers
What is the primary reason behind RDD becoming outdated in Spark?
Which major factor contributed to Spark replacing Hadoop MapReduce?
What is the main advantage of using DataFrames over RDDs in Spark?
How does Spark manage the schema of data when using DataFrames?
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What distinguishes DataFrames from RDDs in terms of data organization?
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Why did Spark introduce DataFrames as a new way of working with data?
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Which feature of DataFrames makes them easier to work with than native RDDs?
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What role does the schema play in the efficiency of DataFrames?
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Study Notes
Pandas vs Spark DataFrame
- Complex operations are easier to perform with Spark DataFrame compared to Pandas DataFrame.
- Spark DataFrame is distributed, making processing faster for large amounts of data.
- Pandas DataFrame is not distributed, making processing slower for large amounts of data.
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sparkDataFrame.count()returns the number of rows, whilepandasDataFrame.count()returns the number of non-NA/null observations for each column.
Spark DataFrame
- Excellent for building scalable applications.
- Assures fault tolerance.
- Can't be used to build a scalable application without implementing a custom framework.
Spark Streaming
- Micro-batch architecture, treating streams as a series of batches of data.
- Replicates data across nodes and uses them in case of issues.
- Tracks RDD block creation process and rebuilds a dataset when a partition fails.
Comparing Spark and Hadoop
- Spark is faster than Hadoop MapReduce, with in-memory processing.
- Spark is 100 times faster than MapReduce for big data processing.
- Spark supports real-time processing through Spark Streaming.
- Spark is not bound to Hadoop and is highly efficient.
Map Reduce
- Does not leverage the memory of the Hadoop cluster to maximum.
- Speed is slow.
- Disk usage is high.
- Only batch processing is supported.
- Is bound to Hadoop.
- Inefficient due to disk writes.
Comparison of Hive and Spark
- Hive uses MapReduce algorithm to process data stored in HDFS.
- Spark stores intermediate results in memory, but also writes to disk if necessary.
- Spark does in-memory processing.
- YARN allows Hadoop to run non-MapReduce jobs, such as Spark, within the Hadoop framework.
Spark and Hadoop
- Spark became an alternative to MapReduce for parallel processing on distributed data.
- Spark does not have a file system of its own, relying on HDFS or other solutions for storage.
- Spark can work with Hadoop, Relational DB, NoSQL DB, and cloud systems like AWS and Azure.
When to Use Spark
- When speed is required, using in-memory processing.
- For live streaming data through Spark Streaming.
- For machine learning, using Spark ML.
- For generality, combining different processing models seamlessly in the same application.
RDD and DataFrame
- RDD is a critical core component of Spark, but is outdated.
- DataFrame is a more efficient and desirable data abstraction for structured and semi-structured data.
- DataFrame stores data in a more efficient manner than native RDDs, using schema and in-memory processing.
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Description
Learn about Apache Spark, a high-speed data processing engine that utilizes memory efficiently, stores data in memory with the use of Resilient Distributed Datasets (RDDs), and supports real-time processing through Spark streaming. Understand how Spark is faster than MapReduce for big data processing due to its in-memory caching and low latency abilities.
