Top 8 Big Data Platforms and Tools in 2024 | Turing (2024)
What are the three major big data platforms?
The three major big data platforms prominently used in the industry are Apache Hadoop, Apache Spark, and Cloudera. Each of these big data platforms offers the latest features and functionalities to help businesses with big data processing needs.
Google BigQuery is an example of a modern data platform.
What is the need of a big data platform?
A big data platform is essential because it enables organizations to effectively manage and analyze large volumes of complex data. Traditional data processing tools and systems are often inadequate for handling the scale and variety of data generated today. Big data platforms provide the infrastructure, tools, and frameworks necessary to collect, store, process, and analyze massive datasets.
What are the essential components of a big data platform?
The essential components of a big data platform typically include data collection, data storage, data processing, and data analysis. Data collection involves gathering and acquiring large volumes of data from various sources. Data storage involves storing the collected data in a centralized repository, such as a data lake or a data warehouse. Data processing involves transforming and manipulating the data. Data analysis involves extracting insights and patterns from the processed data using techniques like data mining, machine learning, and statistical analysis.
Apache Hadoop. Apache Hadoop is one of the industry's most widely used big data platforms. It is an open-source framework that enables distributed processing for massive datasets throughout clusters.
Big data is evolving with time and has increased its focus on artificial intelligence systems and machine learning to enhance and improve business processes. However, edge computing is considered to be the future after big data as it supports and complements data processing via cloud integrations.
In the future, analyzing data will be easier for businesses, big or small. This is because new tools and platforms are being made that make it simpler to use artificial intelligence (AI). AI helps in understanding and using data better. This trend of making AI accessible to everyone is expected to continue in 2024.
Apache Hadoop. Apache Hadoop is an open-source framework for distributed storage and extensive data set processing through simple programming models. ...
Big data technologies can be categorized into four main types: data storage, data mining, data analytics, and data visualization [2]. Each of these is associated with certain tools, and you'll want to choose the right tool for your business needs depending on the type of big data technology required.
Snowflake is the strongest of the Hadoop alternatives in big data management. Through its partnership with cloud Big Data as-a-service-company Qubole, Snowflake maximizes its data warehouse potential. The partnership enables customers to use Apache Spark in Qubole with data stored in Snowflake.
Databricks is a powerful platform for big data analytics and machine learning. With such a data platform, you can gain more efficient insights and make better decisions based on data. Databricks is built on top of Apache Spark and provides advanced capabilities for managing and processing large data sets.
Apache Hadoop. Apache Hadoop is an open-source framework for distributed storage and extensive data set processing through simple programming models. ...
Best Big Data Databases for Comparison. According to the Forrester Wave report, some of the best databases for data analytics and processing are Amazon DynamoDB, Azure Cosmos DB, and MongoDB.
IBM is the leading supplier of Big Data-related products and services. IBM Big Data solutions include features such as data storage, data management, and data analysis. Amazon - Amazon's cloud-based platform is well-known. It also provides Big Data products, the most notable of which is Hadoop-based Elastic MapReduce.
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