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Spark Consulting

Spark is an open-source, lightning fast, cluster computing framework that provides a fast and powerful engine for large-scale data (Big Data) processing. It runs programs up to 100x faster in-memory and 10x faster on disk when compared to Hadoop’s MapReduce system. The reason for Spark’s success is its ability to process data in-memory (using RAM) that allows faster retrieval of data as compared to querying and searching on disk. It is to be noted that Spark is still the fastest technology for disk processing and holds a world-record for large scale on-disk sorting. It has the ability to break down complex queries into multiple computations for parallel processing, which makes it the perfect choice for Big Data analytics and Machine Learning applications. Large organizations love Spark due to its simplicity, flexibility and high-performance data processing power.

Spark’s faster adoption in large number of Fortune 500 companies across various industries shows how remarkable it is. According to a survey done in 2016, more than 1,000 companies used Spark in production, out of which some well-known players like Amazon, Uber, Netflix, eBay, Yahoo and many more are at the top of the list. They have deployed Spark at a large scale, processing petabytes of data. Spark’s largest known cluster so far has been over 8,000 nodes.

Though Spark has been completely written in Scala, it provides high-level APIs for languages like Scala, Java, Python and R. It gives almost 100 high-level operators that makes it easy to build parallel apps. Spark runs on Hadoop YARN, Apache Mesos, Cloud and standalone cluster mode and can access diverse data sources including HDFS, Cassandra, HBase, and S3.

What is RDD in Spark?

RDD stands for Resilient Distributed Dataset and is the primitive type in Spark that holds immutable collection of objects, which can be processed in parallel across multiple nodes of the cluster. Basically, RDDs are read-only and can be created through rough operations like map, filter, group-by, etc. on data from stable or external storage.

Existing computing systems that use MapReduce for processing data need some kind of a storage system (ex: HDFS) and the process of replication, serialization and disk IO in such a system makes it time consuming. RDDs on the other hand enable fault tolerant distributed in-memory computations. In case some part of RDD is lost, it can easily be recovered through transformation on the partition rather than replicating data across multiple nodes, thus RDD reduces loads of data management and replication efforts.

To know more about RDD, breaking it down into these three words help us understand it better:

  • Resilient: It is fault-tolerant, which means that in case of any failure Spark can recover data relatively easily.
  • Distributed: Represents its ability to store data across multiple nodes.
  • Dataset: Users can load data through JSON, CSV and Text files as well as databases via JDBC.

Spark Ecosystem

Spark has an ecosystem that supports multiple programming languages, components/libraries, cluster and storage management. The diagram below represents this ecosystem. In this ecosystem, Spark Core is the main engine and the most important component. It contains components/libraries that help in task scheduling, memory management, fault recovery, interacting with storage systems, etc.

Spark Ecosystem

Spark Libraries/Components

Spark provides a wide range of benefits over other Big Data technologies like Hadoop and MapReduce. It provides advanced Big Data analytics with the support of its libraries.

  • Spark SQL Queries & Data Frames: This library offers a uniform access to a range of different structured data sources such as Apache Hive, Avro, Parquet, ORC, JSON, JDBC/ODBC, etc. It allows data scientists to write SQL queries that can be executed across clusters and combine data sources without the need for complicated ETL pipelines. It has the capability to expose various datasets over JDBC API and allow running SQL-like queries on Spark data using traditional BI and visualization tools. It allows businesses to implement ETL functions on their Big Data from different formats, transform it, and expose it for ad-hoc querying.
  • Spark Streaming: A live stream of data means continuous flow of data from a single or multiple source. Spark Streaming provides a scalable, high-throughput and fault-tolerant stream processing of live data streams. It can ingest data from multiple sources like Kafka, Apache Flume, Amazon Kinesis or TCP sockets. The ingested data is then processed using high-level API operations like map, reduce, join, window, etc. Finally, the processed data is moved/stored in filesystems, live dashboards or databases. The biggest advantages of Spark Streaming are performance, unified batch, streaming and interactive analytics, fast failure recovery and dynamic load balancing.
  • Spark Machine Learning Library: A lot of Data Scientists prefer to use Spark’s scalable Machine Learning library that enables clustering, collaborative filtering and dimension reduction. This library consists of machine learning algorithms and utilities that includes Regression, Clustering, Classification, Decision trees, Random forests, Collaborative filtering, Dimensionality reduction, Topic Modeling and underlying optimization primitives. Its workflow utilities include feature transformation, machine learning pipeline construction, model evaluation and hyper-parameter tuning and persistence mechanism.
  • Spark GraphX: This is a first-class graph analytics engine and data store that can be used to perform loads of graph analytics functions. Clustering, classification, traversal, searching, and pathfinding is also possible in GraphX. It provides flexibility and resilience in graph construction and transformation to Data Scientists that many other tools fail to provide. This API introduces Resilient Distributed Graph (RDG- an abstraction of Spark RDDs) that associates records with the vertices and edges in a graph.

Benefits of using Spark

  • Lightning Fast Speed: Speed is the most important factor when it comes to Big Data. Spark is one such technology that is extremely fast when compared to Hadoop MapReduce. On one side where Hadoop’s MapReduce require a data storage for persisting data, whereas Spark can process data 100x faster in-memory, and 10x faster on disk. Spark’s concept of RDD - Resilient Distributed Dataset stores data in memory and saves it to a disk only when required. This considerably improves performance.
  • Ease of Use: Developers love Spark because it allows them to create applications using various languages like Scala, Python, Java, R. It also provides more than 100 high-level built-in operators that simplifies data processing. It also enables querying data using Shell aka Spark Shell.
  • Advanced Analytics: Spark comes in with its great ecosystem which contains components like SQL Queries & Data Frames, Streaming Data, Graph Algorithm and Machine Learning library (MLlib). It also supports both Map and Reduce operations. Its machine learning library is one of the most famous and preferred library chosen by Data Scientists.
  • Fault Tolerance: The term Fault refers to failure. In case of any processing failure, there are huge chances of data loss. Spark overcomes this issue by replicating data among multiple Spark executors in worker nodes in the cluster, but failure can occur in worker and master nodes as well. This is where Apache Mesos comes in making the Spark master fault tolerant by maintaining the backup masters. Post failure, executors are relaunched automatically and spark streaming does parallel recovery by re-computing Spark RDDs on input data. Receivers are restarted by the workers when they fail.
  • Near Real-Time Processing: The demand for real-time data processing is increasing and Spark’s Streaming API can manage and analyze large volumes of data in real time.
  • Integration with Hadoop and its ecosystem: One of the biggest advantage of Spark is that its application can run on Hadoop YARN, Apache Mesos, Cloud and standalone cluster mode. It’s easy integration and support for Hadoop gives it a big advantage for Big Data analytics.

Comparing Spark with Hadoop:

Spark and Hadoop are the 2 most buzzed about technologies for Big Data Analytics and Machine Learning. There are quite a lot of differences between the two, but when they are combined together, they perform exponentially better.

Hadoop which is widely known as a distributed data infrastructure, stores data across multiple nodes within a cluster of inexpensive servers. Other than storing, it also indexes and keeps track of stored data that helps in big data analytics. Spark, on the other hand, is a data processing tool that operates with distributed datasets over various storage mechanism.

Hadoop’s MapReduce feature, which is responsible for data processing persists data in its distributed file system called HDFS. This process of handling and manipulating data over a physical storage mechanism is time consuming. In contrast, Spark has the concept called RDD that enables processing datasets in-memory that makes it extremely fast when compared to MapReduce.

Both of these technologies are fault-tolerant, but their mechanism of achieving this is different. In the case of Hadoop, when data is lost due to node failure, it can quickly recover it since it is replicated across multiple nodes. Spark, though similar, takes a different approach. The data objects, which are stored in-memory when lost can be recovered through RDD. The difference is how they handle storage; one recovers data from a disk and the other one from memory.

These technologies are not interdependent. Hadoop along with HDFS has its processing component called MapReduce, which is responsible for data processing.

This means that it does not require Spark to process data. Spark on the other hand can process data in memory, but needs a storage mechanism. Other than HDFS, Spark can persist data on Cloud, RDBMS or NoSQL databases and its standalone cluster. Though this is the difference, when Hadoop and Spark are combined together, they provide benefits like extremely fast data processing and a cheap storage mechanism.

Spark and Kafka:

Spark is known as a distributed data processing engine whereas Apache Kafka is a message broker that receives real-time streams of data. Kafka's messaging broker contains a producer and consumer, but they do not have any idea about each other. Kafka acts as a mediator between the two and passes data in a specific format. Spark's Streaming API, which waits for live data stream can receive this data from Kafka. The streaming API can then process that data and either return it back to Kafka or persist it in its storage system. This is how Spark and Kafka can be combined to work together.

How can Cazton help you with Spark Consulting?

Cazton has been a pioneer in Big Data & Hadoop Consulting Our team of Big Data Specialists, Data Scientists, Hadoop Experts, Spark Developers and Consultants, Kafka Consultants have years of experience and strong analytical and problem-solving skills. Our Spark experts have hands-on experience with Big Data technologies that includes Hadoop, HIVE, HBase, Kafka, Impala, PIG, Zookeeper, Cassandra. NoSQL databases like Couchbase, MongoDB and have proven record building solid production level software on Spark and Hadoop. A high-level expertise in programming languages like Scala, Python, Java and R along with Spark Components like Spark Streaming, SQL Queries & Data Frames, Spark Machine Learning library, SparkR and Spark GraphX make them a great resource for your business requirements.

Want to work with world class experts on these technologies? Given our track record of successful projects, Cazton is uniquely positioned to provide a high return on investment. We offer flexible arrangements where you can hire our experts full-time, hourly, or contract-to-hire. And yes, we even accept fixed-bid projects.

Apart from being experts in Spark, we specialize in .NET technologies, Microsoft Dynamic CRM, Cloud Computing, Salesforce, Agile Methodologies, Software Architecture, Industry Standard Design Principles and Patterns, Big Data and Big Data related technologies like Apache Kafka, Apache Hadoop, PIG, Cassandra, HBase, HIVE, Zookeeper, Solr, and ElasticSearch just to name a few. Check out our consulting services for more details.

Cazton has expanded into a global company servicing clients not only across the United States, but in Europe and Canada as well. In the United States, we provide our Spark services across various cities like Austin, Dallas, Houston, New York, New Jersey, Irvine, Los Angeles, Denver, Boulder, Charlotte, Atlanta, Orlando, Miami, San Antonio, San Diego and others. Our Spark Experts remain committed to the vision of helping our clients innovate and transform their business strategies into deliverable projects and real-time solutions. Contact us today to learn more about what our experts can do for you.

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