Anil Kondla
Last Updated on June 11, 2024 by Team Experts
Making decisions no longer relies on gut feelings. Data analytics has made it a more sorted and simpler procedure. So, you just have to prepare your data for processing. This is the process of transforming raw pieces of data into actionable insights. As individuals, businesses, and organizations become more dependent on real-time analytics, it is necessary to automate processing. Here comes the need for data processing tools.
This post will help you find the most popular and highly effective business data processing tools that are designed to meet the needs of modern businesses.
1. Apache Hadoop
Apache Hadoop is a popular processing tool, and it ranks highly in terms of rating because of its amazing features. It is not a single tool, but the wide range of open-source software utilities makes it incredible. These software utilities use a computer’s network or programming models to address data-based problems. Besides, this tool is scalable, which means that its storage from single servers can be shifted to thousands of machines with the support of the MapReduce programming model.
A survey by Datamation proves that over 50% of Fortune 500 companies rely on it. Facebook and LinkedIn are its perfect examples, which continue to scale up data for management and processing.
Key Features
- Hadoop Distributed File System (HDFS): It is actually a distributed file system that uses commodity hardware to run and is also, fault-tolerant.
- MapReduce: This is a programming aspect that simplifies massive scalability for processing across hundreds or thousands of servers.
- YARN: This is one of the core components that help in allocating system resources to various applications running on this tool. Also, it schedules processing to be executed on diverse cluster nodes.
2. Apache Spark
This is also an open-source data processing system, which is also distributed to share the workloads of processing big data. Being able to process heavy-duty tasks with laser-fast speed, it can efficiently enable cluster computing while processing data in parallel across various nodes. It can automatically distribute data processing tasks among various machines.
This tool is reportedly 100x faster than Hadoop MapReduce for certain applications, according to Databricks. But still, its users have grown to over 1K from just 250 in the beginning.
Key Features
- In-Memory Computing: Its in-memory computing takes place in the main Random Access Memory (RAM) of all dedicated servers. This capacity speeds up the processing of data-intensive applications, minimizing data access latency and also, supporting real-time analytics.
- Advanced Analytics: Being an open-source and distributed data processing system, it offloads the burden of overwhelming processing. The aforementioned (in-memory computing) feature also uses in-memory caching and optimized queries for real-time data analytics.
- Compatibility: Apache Spark is compatible with Windows and UNIX-like systems, which can be Mac OS or Linux. Overall, it supports any platform that has a supported version of Java. Its versatility can be observed by using it with Scala, Python, and R. But, it will be an uphill battle to discover a compatible version of this tool.
3. Tableau
Tableau consists of a set of premium data capabilities, including an analytics data catalog, data preparation or flow management, and governance and security controls. It can help with the entire extract, transform, and loading (ETL) process while designing data architecture in charts, graphs, etc.
This tool holds a significant place with Gartner in the domain of business intelligence. Over 70K organizations are its users, including Verizon.
Key Features
- Data Connectivity: It allows users to create and share a central access point to your data source with row level security. Overall, a self-serve facility is there with complete data security. With it, you can curate relevant tables and consolidate extracts and queries with simplified data management.
- Data Governance: It helps in defining data security policies, enabling easy configuration of row-level security for downstream assets. So, the data remains visible to relevant groups and individuals without bouncing out of Tableau. It allows for the provision of context on fresh data, its usage, and its meaning within dashboards through its cataloging capabilities.
- Preparing Data: It enables the ETL process by scheduling data flow on a specific cadence through its Prep Conductor.
4. Microsoft Power BI
Microsoft Power BI is actually a series of software services, applications, and connectors that enable visualization, data compiling, and driving insights. Its components work to transform unrelated data into understandable, visually engaging, and interactive insights. Overall, it can help in collating data from an Excel spreadsheet or cloud-based and on-premises hybrid warehouses.
Because of its simplicity and power-packed features, Power BI is a favorite of over 200,000 organizations, which also include 97% of the Fortune 500 companies.
Key Features
- Draw Insights: It enables you to transform data into visuals with the support of advanced data-analysis tools, AI features, and comprehensive report creation tools.
- Compile Data: It helps in collating datasets from any source into the OneLake data hub.
- Insights into Intelligence: It helps in making better decisions by integrating insights into the apps you have on your system or phone.
5. Apache Kafka
Kafka is helpful in real-time streaming of data through applications and data pipelines. It enables users to use messaging, storage, and stream processing on a platform. Overall, one can store and analyze archived and real-time data.
This tool is capable of processing millions of messages every second, allowing large scale data to be processed seamlessly. Because of these amazing qualities, renowned companies like LinkedIn, Netflix, and Uber process real-time data streams.
Key Features
- Scalability: Its scalability feature enhances the tolerance capacity of this tool. It allows data distribution across many broker nodes and replicas, even if the node fails. Besides, scaling horizontally provides redundant data, which minimizes the chance of data loss.
- Durability: It is a distributed event streaming platform, structured to replicate data so that it can be there across multiple servers and nodes.
- Integration: It ensures the transition of message data and content after registering its topic as a Maximo Manage message queue.
Conclusion
Overall, there are many of the of the best data processing tools that can be used to simplify processing records. The aforementioned tools are the topmost data processing tools on the basis of their user base and usability.
