There is a lot of buzz around What is Big Data, but there is also a lot of confusion as to why it is so important.
How big is big data – First Lets Understand it
Big Data is a combination of structured and unstructured data collected by organizations to extract information. It’s later used for machine learning, predictive modeling, and other analytics applications.
Big data is growing out to be significantly crucial for companies as they utilize it to modify their operations, resulting in better customer service, personalized marketing campaigns by checking on specific customer preferences, and ultimately increasing sales.
An organization using big data has the upper hand over businesses that haven’t yet.
Big data increases the competitive advantage by letting the company know more about their customer and making better decisions.
In an era of massive marketing, setting your target right can garner big customers.
If you aren’t familiar with your customers, there’s no point in spending millions on marketing and customer engagement.
However, companies gain valuable insights into their customer preference, choices, and purchase habits that result in improved customer engagement and conversion rates through big data use.
Apart from retailers, big data use has also helped medical researchers to identify disease risk factors and diagnose illnesses.
It is becoming more and more critical for keeping people healthy.
Data from Electronic health records (EHRs), web, social media and other areas illustrate updated insights on infections, disease threats or outbreaks of a person to the healthcare provider or the government agencies.
Apart from healthcare, big data also helps determine potential drilling locations and regular pipeline operations for the energy industry.
The Breakdown
Big data comprises a wide range of data types, including structured data in databases and data warehouses based on Structured Query Language (SQL), unstructured data, such as text and documents filed stored in Hadoop clusters or NoSQL database systems.
All different ranges of data types can be stored together in a data lake.
It is based on a cloud object storage service or Hadoop.
Big data applications comprise numerous data sources that might not be integrated.
Data analytics is taken from all different places to project a product’s potential success and conversion rates by correlating past sales data, online buyer review or return data.
The question is at what velocity can the big data be generated, processed and analyzed?
In most cases, big data sets are updated daily, weekly or every month in traditional data warehouses.
Big data applications take the incoming data, correlate and analyze it to form an answer/result based on the overarching query.
Data scientists and analysts process a detailed understanding of the present data and find their answers from it that’s valid and up to date.
Big data can be described by the following characteristics:
- Volume
- Variety
- Velocity
- Variability
Storage and Processing of Big Data
The amount of big data handling is a unique challenge on the underlying compute infrastructure.
The computing power necessary to process high volumes and types of data isn’t possible with a single server or server cluster.
To acquire the velocity required to achieve big data tasks, businesses require adequate processing capacity, which asks for hundreds or thousands of servers to process work and operate together in clustered architecture with technology like Apache Spark and Hadoop.
Attaining the required velocity at an affordable range is also a big challenge.
It’s challenging to invest in an extensive server and storage infrastructure that supports big data workloads, especially those that don’t run 24/7.
Hence, public cloud computing is used for hosting big data systems.
Public cloud computing stores petabytes of data and scales up the required servers long enough to complete a big data analytics project.
With the public cloud, the business only has to pay for the storage and compute time and can turn it off whenever needed.
To enhance service levels, even more, the public cloud offers big data capability via managed service, including Amazon EMR, Microsoft Azure HD Insight, and Google Cloud Dataproc.
In cloud environments, big data is stored in Hadoop Distributed File System (HDFS), lower-cost cloud storage (S3), relational databases and NoSQL databases.
The organization that needs to deploy on-premises big data systems use Apache open source technologies in addition to Hadoop and Spark.
More include YARN, MapReduce, Kafka, HBase and SQL-on-Hadoop.
Challenges of Big DATA
Apart from the processing capacity and cost problems, designing significant data architecture is also a big challenge for the users.
Big data systems need to be designed according to the particular needs of the businesses.
Despite designing, deploying and managing extensive data systems also needs new skills from database administrators (DBAs) and developers focused on relational software.
Although all these issues can be resolved using a managed cloud service, IT managers need to be attentive about cloud usage to ensure the charges don’t exceed the limit.
Shifting on-premises data sets and processing workloads to the cloud is also a complex process for many businesses.
To make the data in big data systems accessible to data scientists and analysts is also tricky, especially in distributed environments that comprise different platforms and data stores.
To make relevant data accessible for analytics, IT and analytics work hard to build data catalogs that use metadata management and data lineage functions.
Also, data governance and data quality are a priority to ensure that big data sets remain clean, consistent, and used rightly.
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