The respective architectures of Hadoop and Spark, how these big data frameworks compare in multiple contexts and scenarios that best fit each solution.
Hadoop and Spark, both developed by the Apache Software Foundation, are widely used open source frameworks for big data architectures. Each framework contains an extensive ecosystem of open source technologies that prepare, process, manage, and analyze big data sets.
What is Apache Hadoop?
Apache Hadoop is an open-source software utility that allows users to manage large data sets (from gigabytes to petabytes) by allowing a network of computers (or “nodes”) to solve vast and intricate data problems. It is a highly scalable and cost-effective solution that stores and processes structured, semi-structured, and unstructured data (e.g., internet clickstream logs, web server logs, IoT sensor data, etc.).
The benefits of the Hadoop framework include the following:
- in the midst
- High scalability from a single server to thousands of machines
- Real-time analytics for historical analysis and decision-making processes
of a hardware failure
What is Apache Spark
? Apache Spark
which is also open source, is a data processing engine for large data sets. Like Hadoop, Spark divides large tasks into different nodes. However, it tends to run faster than Hadoop and uses random access memory (RAM) to cache and process data instead of a file system. This allows Spark to handle use cases that Hadoop cannot.
The benefits of the
Spark framework include the following:
- A unified engine that supports SQL queries, streaming data, machine learning (ML),
- It can be 100 times faster than Hadoop for smaller workloads through in-memory processing, disk data storage, etc.
- APIs designed for ease of use when manipulating semi-structured data and transforming data
and graph processing
Hadoop Hadoop supports advanced analytics for stored data (e.g., predictive analytics, data mining, machine learning (ML), etc.). It allows big data analytics processing tasks to be broken down into smaller tasks. Small tasks are performed in parallel by using an algorithm (e.g., MapReduce), and then distributed across a Hadoop cluster (i.e., nodes performing parallel calculations on large data sets).
ecosystem consists of four main modules:
- Hadoop Distributed File System (HDFS): A primary data storage system that manages large data sets running on commodity hardware. It also provides access to high-performance, high-fault-tolerance data.
- Yet Another Resource Negotiator (YARN): Cluster resource manager that schedules tasks and allocates resources (for example, CPU and memory) to applications.
- Hadoop MapReduce: Divide big data processing tasks into smaller ones, distribute small tasks among different nodes, and then execute each task.
- Hadoop Common (Hadoop Core): Set of common libraries and utilities on which the other three modules depend.
Apache Spark ecosystem, the largest open source project in data processing, is the only processing framework that combines data and artificial intelligence (AI). This allows users to perform large-scale data transformations and analysis, and then run state-of-the-art machine learning (ML) and artificial intelligence algorithms.
ecosystem consists of five main modules
: Spark Core: The
- underlying execution engine that schedules and distributes tasks and coordinates input and output (I/O) operations
- SQL: Collects information about structured data to allow users to optimize structured data processing.
- Spark Streaming and Structured Streaming: Both add flow processing capabilities. Spark Streaming takes data from different streaming sources and splits it into microbatches for continuous streaming. Structured streaming, based on Spark SQL, reduces latency and simplifies programming.
- (MLlib): A set of machine learning algorithms for scalability, plus tools for selecting features and building machine learning pipelines. The main API for MLlib is DataFrames, which provides uniformity across different programming languages such as Java, Scala, and Python.
- GraphX: Easy-to-use computing engine that enables the interactive construction, modification, and analysis of scalable, graph-structured data.
Machine Learning Library
of Hadoop and Spark
Spark is an enhancement of Hadoop with MapReduce. The main difference between Spark and MapReduce is that Spark processes and retains the data in memory for subsequent steps, while MapReduce processes the data on disk. As a result, for smaller workloads, Spark’s data processing speeds are up to 100 times faster than MapReduce.
In addition, unlike the two-stage execution process in MapReduce, Spark creates a directed acyclic graph (DAG) to schedule tasks and orchestrate nodes across the entire Hadoop cluster. This task tracking process enables fault tolerance, which reapplies logged operations to data in a previous state.
Let’s take a closer look at the key differences between Hadoop and Spark in six critical contexts
- Performance: Spark is faster because it uses random access memory (RAM) instead of reading and writing intermediate data to disks. Hadoop stores data in multiple sources and processes it in batches through MapReduce.
- Cost: Hadoop runs at a lower cost, as it relies on any type of disk storage for data processing. Spark runs at a higher cost because it relies on in-memory computations for real-time data processing, requiring you to use large amounts of RAM to power up nodes.
- Processing: Although both platforms process data in a distributed environment, Hadoop is ideal for batch processing and linear data processing. Spark is ideal for real-time processing and processing live unstructured data streams.
- Scalability: When data volume grows rapidly, Hadoop scales quickly to meet demand through Hadoop Distributed File System (HDFS). In turn, Spark is based on fault-tolerant HDFS for large volumes of data.
- Security: Spark enhances security with authentication through shared secrets or event logging, while Hadoop uses multiple authentication and access control methods. While Hadoop is generally more secure, Spark can integrate with Hadoop to achieve a higher level of security.
- Machine Learning (ML): Spark is the top platform in this category because it includes MLlib, which performs iterative ML calculations in memory. It also includes tools that perform regression, classification, persistence, pipeline construction, evaluation, etc.
misconceptions about Hadoop
and Spark are cheap: Although
- open source and easy to set up, keeping the server up and running can be expensive. When using features like in-memory computing and network storage, big data management can cost up to $5,000 USD.
- Hadoop is a database: Although Hadoop is used to store, manage, and analyze distributed data, there are no queries involved when extracting data. This makes Hadoop a data store rather than a database.
- Hadoop doesn’t help SMBs: Big data isn’t exclusive to “big business.” Hadoop has simple features like Excel reports that allow smaller businesses to harness its power. Having one or two Hadoop clusters can greatly improve the performance of a small business.
- Hadoop is difficult to configure: Although Hadoop administration is difficult at higher levels, there are many graphical user interfaces (GUIs) that simplify programming for MapReduce.
Common Misconceptions About
Spark Spark is an in-memory technology:
- Although Spark effectively uses the least recently used algorithm (LRU), it is not, in itself, a memory-based technology
- can perform up to 100x faster than Hadoop for small workloads, according to Apache, it typically only works up to 3x faster for large ones.
- introduces new technologies in data processing: although Spark effectively uses the LRU algorithm and pipeline data processing, these capabilities previously existed in massively parallel processing (MPP) databases. However, what sets Spark apart from MPP is its open-source orientation.
. Spark always runs 100x faster than Hadoop: Although Spark
Hadoop and Spark Use Cases Based on the
comparative analyses and factual information provided above, the following scenarios best illustrate the overall usability of Hadoop versus Spark.
Hadoop Use Cases Hadoop
is most effective for scenarios that involve:
- Processing large data sets in environments where data size exceeds available
- memory Batch processing with tasks that take advantage
- of disk read and write operations Building data analytics infrastructure on a limited budget
- Complete jobs that are not time sensitive
- of historical and archival data
Cases Spark is most effective for scenarios that involve:
- Dealing with chains of parallel operations by using iterative algorithms
- Get fast results with in-memory calculations
- Real-time flow data analysis
- Graphical parallel processing to model
ML applications Hadoop, Spark and IBM
IBM offers multiple products to help you leverage the benefits of Hadoop and Spark to optimize your big data management initiatives while Achieves your end-to-end business goals:
- IBM Spectrum Conductor is a multi-tenant platform that deploys and manages Spark with other application frameworks in a common, shared resource cluster environment. Spectrum Conductor offers workload management, monitoring, alerting, reporting and diagnostics and can run multiple current and different versions of Spark and other frameworks at the same time.
- IBM Db2 Big SQL is a hybrid SQL engine on Hadoop that provides a single database connection and delivers advanced, security-rich data queries across big data sources such as Hadoop HDFS and WebHDFS, RDMS, NoSQL databases and object stores. Users benefit from low latency, high throughput, data security, SQL support, and federation capabilities for ad hoc and complex queries.
- Big Replicate unifies Hadoop clusters running on Cloudera Data Hub, Hortonworks Data Platform, IBM, Amazon S3 and EMR, Microsoft Azure, OpenStack Swift and Google Cloud Storage. Big Replicate provides a clustered virtual namespace and cloud object storage over any distance.