What do you understand by Big Data Analytics?
Big data analytics is the use of advanced analytic techniques against massive data volumes that includes data from different sources in different formats. Businesses can gain insights from the large volumes of data available today - thanks to big data analytics. Different sources generate large data sets that include unstructured, semi-structured, and structured data in different sizes. A few examples include organizations, people, social media, cloud applications, and data from machine sensors. With the use of big data analytics tools, it is possible to discover opportunities and identify patterns and risks.
Why is big data analytics important?
Using big data analytics can prove beneficial for organizations in making data-driven decisions that can improvise business operations and enhance outcomes. Big data analytics can maximize operational efficiency, enhance customer experience, and lead to more effective marketing. These benefits over industry peers can be achieved with a well-designed strategy.
Key Big Data Analytics Technologies and Tools
A variety of technologies and tools are used to support big data analytics processes. Below-listed is the common tools & technologies that enable the use of big data analytics:
Hadoop: The open-source framework enables the storage and processing of massive data volumes. Large volumes of structured and unstructured data can be handled with Hadoop.
Predictive analytics: In order to anticipate the outcomes of future events, predictive analytics hardware and software process high volumes of complicated data using machine learning and statistical algorithms. Tools for predictive analytics are used by businesses in operations, marketing, risk assessment, and fraud detection.
Stream analytics: Big data that may be stored in a variety of formats or platforms is filtered, gathered, and analyzed using stream analytics technologies.
Understand how to leverage Big Data for optimizing pharmaceutical value chain with use cases
Providing low-latency access or protecting against independent node failures, and lost or damaged huge data, are some examples of possible uses for this.
Distributed storage data: Usually on a non-relational database, distributed storage data is duplicated. Providing low-latency access or protecting against independent node failures, and lost or damaged huge data, are some examples of possible uses for this.
NoSQL databases: When working with large distributed data sets, NoSQL databases are very useful as non-relational data management systems. They are ideal for raw and unstructured data because they don't need a set format.
Spark: The open-source cluster computing framework used for processing batch and stream data.
Data lake: A data lake is a sizable storage facility where raw data in native formats are kept until they are required. A flat architecture is used by data lakes.
Looking to get more value from your big data?
We break down this question into a series of concrete steps for more effective data managementGet the free ebook
Data warehouse: A data warehouse is a repository used to store high data volumes collected by varied sources. Using predefined schemas, data is stored in data warehouses.
Knowledge of big data mining tools: This allows the mining of huge data sets - structured & unstructured.
Data virtualization: This enables data access to users without causing any technical restrictions.
Data preprocessing software: Preparing data for subsequent analysis through data preparation software. Unstructured data is cleaned, and data is prepared.
Data integration software: This allows big data streamlining across various platforms that include Apache, Hadoop, MongoDB, and Amazon EMR.
Data quality software: Large data sets are cleaned and improved using data quality tools.