big data analytics history

Bigdata history

 

John Graunt dealt with huge data in 1663 while studying the bubonic plague in Europe. Graunt was the first-ever individual to employ statistical data analysis. Statistics grew to incorporate data collection and analysis in the early 1800s.

 

bigdata

Data overload was first noticed in 1880. The US Census Bureau estimated it would take eight years to process the 2010 census data. Herman Hollerith, a Bureau employee, created a calculator in 1881. Data developed quickly in the 20th century. Big data drove development. Magnetic storage machines, message pattern scanners, and computers were also invented. The US government created the first data center in 1965 to store fingerprints and tax filings. Since the 1990s, 'Big Data' has been used. John R. Mashey, a Silicon Graphics employee at the time, is credited with popularizing the word. Big Data isn't new or merely 20 years old. People have used data analysis and analytics to make decisions for ages. Around 300 BC, ancient Egyptians endeavored to collect all library 'data' The Roman Empire analyzed military data to establish best distribution. In the previous two decades, data volume and pace have increased beyond human understanding. The total quantity of data in the globe was 4.4 zettabytes in 2013. By 2020, that will reach 44 zettabytes. 44 zettabytes is 44 trillion gigabytes. Even with modern technology, this data cannot be analysed. Traditional data analysis evolved into 'Big Data' in the previous decade to analyses bigger, unstructured data collections. Big Data's evolution may be loosely separated into three periods to demonstrate its progression. Each phase has unique qualities. To comprehend Big Data today, it's vital to understand how each step contributed.

 

1.0 Big Data

 

Data analysis, analytics, and Big Data stem from database administration. It significantly depends on RDBMS storage, extraction, and optimization methods (RDBMS).

 

Big Data Phase 1 includes database administration and data warehousing. It offers the framework for contemporary data analysis employing database queries, online analytical processing, and standard reporting tools.

 

2.0 Big Data

 

Internet and Web data collecting and analysis started in the early 2000s. Yahoo, Amazon, and eBay began studying click-rates, IP-specific location data, and search logs as web traffic and online storefronts grew. This changed everything. HTTP-based online traffic increased semi-structured and unstructured data for data analysis and Big Data. Organizations required new methodologies and storage solutions to successfully evaluate these new data kinds. Social media data's presence and expansion increased the demand for tools, technology, and analytics approaches to extract useful information from unstructured data.

 

3.0 Big Data

 

Many firms still depend on web-based unstructured material for data analysis, data analytics, and big data, but mobile devices provide new ways to obtain useful information. Mobile devices may evaluate behavioral data (clicks and search queries) and location-based data (GPS-data). With these mobile gadgets, you may monitor mobility, activity, and health data (number of steps you take per day). This data may improve transportation, municipal planning, and health care. Sensor-based internet-enabled gadgets are also enhancing data production. Millions of TVs, thermostats, wearable’s, and even refrigerators generate zettabytes of data daily. The competition to extract important data from new sources has just started.

 

 

Bigdata types

 

 

Structured data

Structured data is stored, accessed, and processed in fixed-format. Businesses analyses this data since it's in a comparable format. Advanced technologies derive data-driven judgments from structured data. The world's generation of structured data has surpassed zettabytes.

 

Data unstructured

Unstructured data is any undetermined shape or organization. Processing unstructured data and analyzing it to achieve data-driven responses is difficult since it comes from multiple categories. Unstructured data includes diverse text files, photos, movies, etc.

 

Data structure

Semi-structured data is structured and unstructured. Semi-structured data is organized in form, but not in relational DBMS. Web data is semi-structured. Log files, transaction history files, etc. are unstructured. OLTP systems use structured, relational data.

Maryam Saeed Dogar

 

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