In the previous article, I introduced the significance of Big Data analytics for digital venture executives. Even though executives usually do not go to the details for tools, they need to choose cost-effective and robust tools to empower their data and analytics practices in small and medium-sized ventures. Open-source is ideal for startup companies.
Open-source is widespread in the technology sector; hence equally crucial for Big Data and analytics tasks in digital ventures. It is a type of licensing agreement that allows the developers and users to freely use the software, modify it, develop new ways to improve it and integrate it into larger projects. Open-source is a collaborative and innovative approach embraced by many business organisations and digitally intelligent consumers.
Open-source tools are ideal for start-up companies and those with a tight technology budget, particularly business organisations struggling to have more flexible architectures for modernising and transforming their digital ventures.
There are many open-source tools and technologies for Big Data and analytics.
In this article, I aim to provide an overview of popular and essential open-source tools used for Big Data and analytics solutions.
An awareness of these tools is fundamental for technology staff and highly recommended for technology executives.
Here’s a summary of the famous open-source Big Data and analytics tools.
Hadoop is a platform for data storage and processing. Hadoop is scalable, fault-tolerant, flexible, and cost-effective. It is ideal for handling massive storage pools using the batch approach in distributed computing environments. Digital ventures can use Hadoop for complex Big Data and analytics solutions on both small and large scales.
Apache Cassandra
Cassandra is a semi-structured open-source database. It is linearly scalable, high speed, and fault-tolerant. The principal use case for Cassandra is transactional systems requiring fast response and massive scalability. Cassandra is also widely used for Big Data and analytics solutions on both small and large scales.
Apache Kafka
Kafka is a stream processing software platform. Using Kafka, users can subscribe to commit logs and publish data to any number of systems or real-time applications. Kafka offers a unified, high-throughput, low-latency platform for real-time handling of data feeds. Kafka platforms were initially developed by LinkedIn, used for a while, and donated to the open-source community.
Apache Flume
Flume offers a simple and flexible architecture. The architecture of Flume is a reliable, distributed software for efficiently collecting, aggregating, and moving large amounts of log data in the Big Data ecosystem. Flume can be sued for streaming data flows. Flume is fault-tolerant with many failover and recovery systems. It uses an extensible data model for online analytic applications.
Apache NiFi
NiFi is an automation tool designed to automate data flow among software components based on a flow-based programming model. Currently, Cloudera organisation supports both commercial and development requirements. It has a portal for the users and uses TLS encryption for security.
Apache Samza
Samza is a near-real-time stream processing system. It provides an asynchronous framework for stream processing. Samza allows building stateful applications that process data in real-time from multiple sources. It is well known for offering fault tolerance, stateful processing, and isolation.
Apache Sqoop
Sqoop is a command-line interface application used to transfer data between Hadoop and the relational databases. It can be used for incremental loads of a single table or free form SQL queries. Ventures can use Sqoop with Hive and HBase to populate the tables.
Apache Chukwa
Chukwa is a system designed for data collection. Chukwa monitors large distributed systems and builds on the MapReduce framework on HDFS (Hadoop Distributed File System). Chukwa is a scalable, flexible, and robust system for data collection.
Apache Storm
Storm is a stream processing framework. Storm is based on spouts and bolts to define data sources. It allows batch and distributed processing of streaming data. Storm also enables real-time data processing.
Apache Spark
Spark is a framework that allows cluster computing for distributed environments. Spark can be used for general clustering needs. It provides fault tolerance and data parallelism. Spark’s architectural foundation is based on the resilient distributed dataset. The Dataframe API is an abstraction on top of the resilient distributed dataset. Spark has different editions, such as Core, SQL, Streaming, and GraphX.
Apache Hive
Hive is data warehouse software. Ventures can build Hive on the Hadoop platform. Hive provides data query and supports the analysis of large datasets stored in HDFS. It offers a query language called HiveQL.
Apache HBase
HBase is a non-relational distributed database. HBase runs on top of HDFS (Hadoop Distributed File System). HBase provides Google’s Bigtable-like capabilities for Hadoop. HBase is a fault-tolerant system.
MongoDB
MongoDB is a high performance, fault-tolerant, scalable, cross-platform and NoSQL database. It deals with unstructured data. MongoDB Inc develops it as licensed under the SSPL (Server-Side Public License), a kind of open-source product.
Conclusion
There are many more rapidly developing open-source software tools that can be used for various functions of data life cycle management in digital ventures.
Open-source tools can be handy for low budget ventures focusing on modernising and transforming legacy data and analytics solutions. They are also agile focussed supporting fast delivery.
These tools are easily accessible from open-source sites and available free based on open-source licencing agreements. There is also substantial volunteer support in open-source communities for these tools.
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