Data drives the modern organizations of the world; it’s widely believed that world is driven by data. Today’s business enterprises owe a huge part of their success to an economy that is firmly knowledge-oriented. The 3 Vs, Volume, Variety, and Velocity of available data have grown exponentially. How an organization defines its data strategy and its approach towards analyzing and using available data will make a critical difference in its ability to compete in the future data world. As there are a lot of options available in the data analytics market these days so this approach includes a lot of choices that organizations need to make like which framework and technologies to use.
Organizations with huge data they often don’t know the hidden potential of their data. They sometimes don’t know how to use that data to analyze and find out potential businesses. We offer our expertise with our professional engineers to use cutting edge technologies and do data analysis for you so that you could focus on your business.
We can help setup your data analysis setups in cloud or on-premises. We have an expertise in Apache Hadoop, HDFS, MapReduce, NoSQL, Hive, Spark, In-Memory Databases, Predictive Analytics, Data Lakes, Streaming analytics, Edge Computing, ELK etc.
Apache Spark is a general-purpose distributed data processing engine that is suitable for use in a wide range of circumstances. On top of the Spark core data processing engine, there are libraries for SQL, machine learning, graph computation, and stream processing, which can be used together in an application.
Spark is capable of handling several petabytes of data at a time, distributed across a cluster of thousands of cooperating physical or virtual servers. It has an extensive set of developer libraries and APIs and supports languages such as Java, Python, R, and Scala; its flexibility makes it well-suited for a range of use cases. Spark is often used with distributed data stores such as MapR XD, Hadoop’s HDFS, and Amazon’s S3, with popular NoSQL databases such as MapR-DB, Apache HBase, Apache Cassandra, and MongoDB, and with distributed messaging stores such as MapR-ES and Apache Kafka.
Apache Spark is generally used for Stream processing, Machine learning, Interactive analytics, and Data integration.
Apache Spark is a general-purpose distributed data processing engine that is suitable for use in a wide range of circumstances. On top of the Spark core data processing engine, there are libraries for SQL, machine learning, graph computation, and stream processing, which can be used together in an application.
Spark is capable of handling several petabytes of data at a time, distributed across a cluster of thousands of cooperating physical or virtual servers. It has an extensive set of developer libraries and APIs and supports languages such as Java, Python, R, and Scala; its flexibility makes it well-suited for a range of use cases. Spark is often used with distributed data stores such as MapR XD, Hadoop’s HDFS, and Amazon’s S3, with popular NoSQL databases such as MapR-DB, Apache HBase, Apache Cassandra, and MongoDB, and with distributed messaging stores such as MapR-ES and Apache Kafka. Apache Spark is generally used for Stream processing, Machine learning, Interactive analytics, and Data integration.
Apache Hive is a data warehouse system built on top of Apache Hadoop that facilitates easy data summarization, ad-hoc queries, and the analysis of large datasets stored in various databases and file systems that integrate with Hadoop, including the MapR Data Platform with MapR XD and MapR Database. Hive offers a simple way to apply structure to large amounts of unstructured data and then perform batch SQL-like queries on that data. Hive easily integrates with traditional data center technologies using the familiar JDBC/ODBC interface.
Hive was initially developed at Facebook to summarize, query, and analyze large amounts of data stored on a distributed file system. Hive makes it easy for non-programmers to read, write, and manage large datasets residing in distributed Hadoop storage using HiveQL SQL-like queries. Hive has gained a lot of popularity due to its ease of use and compatibility with existing business applications through ODBC.
Apache Hadoop does not provide random access capabilities, and this is when the Hadoop database HBase comes to the rescue. HBase is high scalable (scales horizontally using off the shelf region servers), highly available, consistent and low latency NoSQL database. With flexible data models, cost effectiveness and no Sharding (automatic Sharding), HBase works well with sparse data.
Apache Hadoop is not a perfect big data framework for real-time analytics, and this is when HBase can be used i.e. For real-time querying of data. HBase is an ideal big data solution if the application requires random read or random write operations or both. If the application requires to access some data in real-time then it can be stored in a NoSQL database. HBase has its own set of wonderful API’s that can be used to pull or push data. HBase can also be integrated perfectly with Hadoop MapReduce for bulk operations like analytics, indexing, etc. The best way to use HBase is to make Hadoop the repository for static data and HBase the data store for data that is going to change in real-time after some processing.
MapReduce is the heart of Apache Hadoop. It is a programming paradigm that enables massive scalability across hundreds or thousands of servers in a Hadoop cluster. The term "MapReduce" actually refers to two separate and distinct tasks that Hadoop programs perform. The first is the map job, which takes a set of data and converts it into another set of data, where individual elements are broken down into tuples (key/value pairs). The reduce job takes the output from a map as input and combines those data tuples into a smaller set of tuples. As the sequence of the name MapReduce implies, the reduce job is always performed after the map job.
A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically, both the input and the output of the job are stored in a file-system. The framework takes care of scheduling tasks, monitoring them and re-executes the failed tasks.
Presto (or PrestoDB) is an open source, distributed SQL query engine, designed from the ground up for fast analytic queries against data of any size. It supports both non-relational sources, such as the Hadoop Distributed File System (HDFS), Amazon S3, Cassandra, MongoDB, and HBase, and relational data sources such as MySQL, PostgreSQL, Amazon Redshift, Microsoft SQL Server, and Teradata.
Presto can query data where it is stored, without needing to move data into a separate analytics system. Query execution runs in parallel over a pure memory-based architecture, with most results returning in seconds. You’ll find it used by many well-known companies like Facebook, Airbnb, Netflix, Atlassian, and Nasdaq.
Kafka is a distributed messaging system providing fast, highly scalable and redundant messaging through a pub-sub model. Kafka’s distributed design gives it several advantages. First, Kafka allows a large number of permanent or ad-hoc consumers. Second, Kafka is highly available and resilient to node failures and supports automatic recovery. In real world data systems, these characteristics make Kafka an ideal fit for communication and integration between components of large-scale data systems.
In short, Kafka is used for stream processing, website activity tracking, metrics collection and monitoring, log aggregation, real-time analytics, CEP, ingesting data into Spark, ingesting data into Hadoop, CQRS, replay messages, error recovery, and guaranteed distributed commit log for in-memory computing (microservices).
Impala is a parallel processing query engine on top of clustered systems like Apache Hadoop. It was created based on Google’s Dremel paper. It is an interactive SQL like query engine that runs on top of Hadoop Distributed File System (HDFS). Impala uses HDFS as its underlying storage.
Storm (Apache Storm) is an open-source distributed real-time computational system for processing data streams. Similar to what Hadoop used to do for batch processing, Apache Storm does for unbounded streams of data in a reliable manner. Apache Storm can process over a million jobs in fraction of a second on a node. Usually it is integrated with Hadoop to harness higher throughputs. It's easy to implement and can be integrated with any programming language
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