Big data is referred to as the combination of structured, semi-structured and unstructured data collected by organizations that may be mined for information and utilized in machine learning projects, predictive modeling and other advanced analytics applications.
Data mining in regards to big data, describes the process by which companies study information to gain insights into consumer behavior. Every modern industry depends on data mining in some way. Big data can be characterized by its size. It consists of datasets so large that they require the help of computer technology to be analyzed. Safe to say without big data, data mining wouldn’t exist. According to Data Science Central, the term big data first emerged in 1997 and was used to refer to data collections that are too large to be captured within an acceptable scope. Recently, the term was redefined several times. The concept as understood today was introduced to the wider public in 2007, according to the World Economic Forum.
Types Of Big Data
- Structured data has a certain set of organizational properties and is present in structured or tabular schema, making it easier to sort and analyze. In addition, due to its predefined nature, each field is discrete and may be accessed separately or jointly together with data from other fields. This makes structured data extremely valuable, making it possible to gather data from various locations quickly within the database.
- Unstructured data requires information with no predefined conceptual definitions and isn’t easily interpreted or analyzed by standard databases or data models. Unstructured data accounts for the majority of big data may comprise information such as dates, numbers, and facts. Big data examples of such include video and audio files, satellite imagery, mobile activity and No-SQL databases, to name a few. Photos uploaded on Facebook or Instagram and videos that we watch on YouTube or any other platform contribute to the growing stack of unstructured data.
- Semi-structured data is seen as a hybrid of structured and unstructured data. It suggests that it inherits a few characteristics of structured data but still contains information that fails to have a definite structure and does not conform with connected databases or formal structures of data models. For instance, XML and JSON are typical examples of semi-structured data.
Big data is often characterized by the following;
- Volume
- Variety
- Velocity
- Veracity
- Value
1. Volume
This is known as the most commonly cited characteristic of big data. Big data environment doesn’t have to contain a large amount of data, but some do because of the nature of the data being collected and stored in them. System logs, clickstreams and stream processing systems are among the sources that typically produce enormous volumes of data on an ongoing basis.
2. Variety
Big data also encircles a wide variety of data types, including structured data such as transactions and financial records; unstructured data, such as text, documents and multimedia files; and semi-structured data like a web server logs and streaming data from sensors. Several data types may need to be stored and managed together in big data systems. Big data applications sometimes include multiple data sets that may not be integrated upfront.
3. Velocity
It is the speed at which data is generated and must be processed and analyzed. Big data sets are sometimes updated in real time, rather than daily, weekly, or monthly in traditional data warehouses. Managing data velocity is critical as big data analysis extends into machine learning and AI, where analytical systems automatically detect patterns in data to provide insights.
4. Veracity
This refers to the level of accuracy in data sets and how trustworthy they are. Raw data collected from several other sources can cause data quality issues that may be difficult to pinpoint. If not fixed through data cleansing processes, bad data leads to analysis errors that can undermine the value of business analytics initiatives. Data management and analytics teams also need to verify that they have enough accurate data available to produce valid results.
5. Value
Some of the data scientists and consultants added value as one of big data’s characteristics because not all the information collected has real business value or benefits.Therefore, organizations must confirm that the data relates to relevant business issues before it is used in big data analytics projects.
Why Big Data Seems Important.
- ❖ Companies make use of big data in their systems to provide better customer service, improve operations, create personalized marketing campaigns and take other actions that can increase revenue and profits.
- ❖ Medical researchers use big data to identify disease signs and risk factors and help doctors to diagnose illnesses and medical conditions in patients.
- ❖ In the energy industry, it helps oil and gas companies identify potential drilling locations and as well monitor pipeline operations, while utilities use it to track electrical grids.
- ❖ Financial services firms use big data systems for real-time analysis and risk management of market data.
- ❖ Manufacturers and transportation companies rely on big data to manage most of their supply chains and optimize delivery routes.
- ❖ Other government uses include emergency response, smart city initiatives and crime prevention.
- Examples Of Big Data.
Big data comes from countless sources and some examples includes;
- Transaction processing systems
- Customer databases
- Documents
- Emails
- Medical records
- Internet clickstream logs
- Mobile apps
- Social networks.
Machine-generated data, such as server log files, network and sensor data from manufacturing machines, industrial equipment, and internet of things (IoTs) devices are included. Big data environments also include external data on financial markets, consumers, geography, weather, traffic, and scientific research. Videos, images, and audio files are also big data, and many big data applications incorporate continuously streaming data.
How Big Data Is Stored And Processed
Big data is regularly stored in a data lake. Data lakes can accommodate multiple data types and are often based on cloud object storage services, Hadoop clusters, NoSQL databases or other big data platforms.
Big data environments use a distributed architecture that mixes platforms. A data lake can be linked to relational databases. A data warehouse or database. Big data systems can leave data raw and then filter and arrange it for analytics. In other circumstances, it’s preprocessed with data mining tools and data preparation software for frequently run applications. Big-data processing places a heavy demand on the underlying computer infrastructure.
Big Data Management Technologies
Big data platforms offered by IT vendors that are currently available includes;
- Amazon EMR (formerly Elastic MapReduce)
- Cloudera Data Platform
- Google Cloud Dataproc
- HPE Ezmeral Data Fabric (formerly MapR Data Platform)
- Microsoft Azure HDInsight
For organizations that would like to distribute big data systems themselves, either on Whether on-premises or in the cloud, the following kinds of tools are available to them in addition to Hadoop and Spark:
- Storage repositories like the Hadoop Distributed File System (HDFS) and cloud object storage services that include Amazon Simple Storage Service (S3), Azure Blob Storage and Google Cloud Storage.
- Stream processing engines, such as Hudi, Flink, Kafka, Samza, Storm and the Spark includes Spark Streaming and Structured Streaming modules.
- NoSQL databases such as Couchbase, Cassandra, CouchDB, HBase, MarkLogic Data Hub, Redis, Neo4j, MongoDB, and a number of more technologies.
- Data warehouse and data lake platforms, among them Amazon Redshift, Delta Lake, Google BigQuery, Kylin and Snowflake.
- SQL query engines such as Drill, Hive, Impala, Presto and Trino.
Challenges Surrounding Big Data
Coupled with the processing capacity issues, designing big data architecture is a common challenge for users. IT and data management teams must put together a unique mix of technologies and tools to tailor big data solutions to an organization’s demands. Big data systems require new abilities from database managers and relational software engineers.
Both of these issues can be assembled by using a managed cloud service, but IT specialists need to keep a close eye on cloud usage to make sure the costs don’t get out of hand. Moving on-premises data sets and processing workloads to the cloud is often a difficult process.
Making big data available to data scientists and analysts is another problem, especially in remote contexts with several platforms and data repositories. Data management and analytics teams are constructing data catalogs with metadata and lineage functions to help analysts identify relevant data. The process of integrating sets of big data is sometimes also complicated, particularly when data
variety and velocity are defining factors.
