Top 6 Big Data Challenges & Solutions for 2023 & Beyond
Introduction
Big data describes data sets that are too big or intricate for conventional data-management software to handle. These data sets are so large that they just cannot be handled by conventional data processing tools. Big data may be leveraged to solve business issues that were previously impossible to solve.
The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three Vs.
The 3-Vs
Volume
Large amounts of low-density, unstructured data need to be analyzed while working with big data. This can be unvalued data from sources like Twitter data feeds, clickstreams from websites or mobile apps, or sensor-enabled hardware. This amount of data may reach tens of gigabytes for some corporations.
Velocity
Some internet-enabled smart goods function in real time or almost real time, necessitating real-time analysis and decision-making. Velocity refers to how quickly data is received and (perhaps) used. In contrast to being written to disk, the maximum velocity of data often streams straight into memory.
Variety
Variety alludes to the wide range of data kinds that are accessible in a relational database. Data now arrives in new unstructured data formats thanks to the growth of big data. Text, audio, video and video are examples of semi-structured and unstructured data formats that require further preprocessing.
Other 2-Vs
Over the past several years, the concepts of worth and truth have also developed. Data has inherent worth. But until that value is realized, it is useless.
Technology advances have dramatically lowered the cost of computing and data storage. Big data makes business decisions more accurate and precise. Value may be found in more ways than only via analysis. It’s a comprehensive discovery process that calls for perceptive analysts, business users, and executives.
Top 6 Big Data Management Challenges
The major big data management challenges include:-
- Storage
- Processing
- Security
- Finding & Fixing Data Quality Issues
- Scaling Big Data Systems
- Evaluating & Selecting Big Data Technologies
Big Data Storage
Real-time data analytics are made possible by big data storage, a compute-and-storage architecture that gathers and maintains enormous data volumes.
Big data’s actual data is unstructured, which mostly uses file- and object-based storage. Big data storage typically refers to volumes that expand rapidly to terabyte or petabyte scale. It is difficult for storage devices to scale, improve access times, and increase data transfer rates. In this huge data era, storage and management are important concerns.
Factors such as capacity, performance, throughput, cost, and scalability are involved in any ideal storage solution system.
The solution to this data management challenge is using Machine Learning (ML), building custom big data storage using Network Attached Storage (NAS) & Object Storage.
Using ML for Big Data Storage
ML algorithms are useful for data collection, analysis, and integration. ML algorithms can be applied to every element of Big data operation, including:
- Data Labelling and Segmentation
- Data Analytics
- Scenario Simulation
ML and big data are combined in an endless cycle. Making sense of it requires massively scalable, high-performance storage, and powerful software intelligence that imposes structure on the raw data. All of these steps work together to extract the big picture from the big data — which includes insights and patterns that are then classified and presented in an intelligible way.
Building Custom Big Data Storage
Big data storage design typically uses scale-out NAS or object systems, or server nodes that are geographically dispersed, such as the Hadoop model. Everyone has benefits and drawbacks. Your infrastructure may be built using a combination of different platforms, depending on the nature of your big data storage needs.
NAS
Shared access to parallel file-based storage is offered by clustered and scale-out NAS systems. With independent scaling of computation and storage, data is dispersed among a large number of storage nodes with a capacity scaling to billions of files. For big data tasks requiring massive files, NAS is advised. Automated tiering is offered by the majority of NAS suppliers to reduce cost per gigabyte and ensure data redundancy.
Object Storage
Object storage archive systems may be expanded to accommodate potentially billions of data, similar to scale-out NAS. An object storage system associates a special identifier with each file instead of using a file tree. Within a flat address space, the objects are displayed as a single controlled system.
A data lake is an extension of the concept of object storage. A data lake, which is frequently related to Hadoop-based big data storage, simplifies the administration of non-relational data dispersed over several Hadoop clusters.
Big Data Processing
Another big data management challenge is big data processing. Big data processing is a collection of methods or programming models for gaining access to enormous amounts of data. Traditional programming paradigms like Message Passing Interface (MPI) cannot manage huge data since it is typically kept on hundreds of commodity servers. Some of the algorithms used to solve this issue are:-
Linear Regression
One of the most fundamental algorithms used in advanced analytics is linear regression. The objective of linear regression is to find the relationship between the independent and dependent variables in the form of a formula. The dependent variable may be anticipated for any occurrence of an independent variable after this connection has been specified.
Logistic Regression
Using logistic regression, it is possible to determine whether a particular occurrence of an input variable falls into a particular category or not. Instead of being continuous and having infinite values like in linear regression, the output variable values in this case are discrete and finite. Results that are closer to 1 show that the input variable matches the category more obviously.
Big Data Security
Security can be one of the most daunting Big Data management challenges especially for organizations that have sensitive company data or have access to a lot of personal user information. When it comes to data security, most organizations believe that they have the right security protocols in place that are sufficient for their data repositories. Only a few organizations invest in additional measures exclusive to Big Data such as identity and access authority, data encryption, data segregation, etc.
The solution to this challenge is:
- Recruiting more Cybersecurity Professionals
- Data Encryption and Segregation
- Identity and Access Authorization Control
- Endpoint Security
- Real-Time Monitoring
- Using Big Data security tools such as IBM Guardium
Data Encryption & Segregation
Data is converted into another form, or code, via data encryption so that only those with a decryption key or password may decipher it. Unencrypted data is referred to as plaintext, whereas encrypted data is frequently referred to as ciphertext. At the moment, corporations employ encryption as one of the most common and successful data security techniques.
Data segregation is the process of separating particular data sets from other data sets so that various access policies can be used with those various data sets. Organizations may need to separate their data for a variety of reasons, including regulatory requirements and systems shared during mergers, acquisitions and divestitures.
Finding & Fixing Data Quality Issues
Ensuring the quality of the data is also one of the major data management challenges. Modern technology and AI are essential for data-driven enterprises to maximize the value of their data assets. But they continually face problems with data quality. Data that is erroneous or incomplete, security issues, hidden data — the list goes on and on.
The common data quality issues include duplicate data, inaccurate data, ambiguous data, hidden data, inconsistent data and so on. This data management issue can be solved by:
- Prioritizing data in the organizational data strategy
- Involve & Enable all Stakeholders
- Incorporate Metadata to Describe & Enrich Data
- Use Data Governance & Data Catalog
Data quality problems might be viewed as chances to address the core causes and save further losses. Utilize your trustworthy data to enhance customer experience, find new business possibilities, and promote expansion with a shared knowledge of data quality.
Some common data quality checks include:
- Identifying duplicates or overlaps for uniqueness.
- Checking for mandatory fields, null values, and missing values to identify and fix data completeness.
- Applying formatting checks for consistency.
- Assessing the range of values for validity.
- Checking how recent the data is or when it was updated last identifies the recency or freshness of data.
- Validating row, column, conformity, and value checks for integrity.
Scaling Big Data Systems
The real problem in this case is the complexity in scaling up, so that even in that case the performance of the system does not decrease. Scaling up systems has been a serious challenge for most of the businesses and so, in case of big data too, scaling is a data management challenge. It’s time to concentrate on the issue of scalability if latency is hindering development too much and customers are complaining about how sluggish your systems are.
The first and most important safeguard against problems like this is a good design for your big data solution. Less issues are likely to arise later if your big data solution can make that claim. Designing your big data algorithms with future upscaling in mind is another crucial step to take.
Estuary’s Flow provides a simpler method for powerful stateful joins. This can be done using an operational transform system such as Spark or Flink. Usually, the data stores are in multiple tables due to which charges for queries rise up and so, by using the above systems, the number of queries and hence the cost can be reduced.
Evaluating & Selecting Big Data Technologies
Selecting the right technology for the company’s needs is another data management challenge. With so many new big data technologies coming up and many more existing, choosing the right one is a tedious task. However, in the recent years, the most potential seeking IT industry trending technologies include:
- Hadoop Ecosystem
- Apache Spark
- NoSQL Databases
- R Software
- Predictive Analytics
- Prescriptive Analytics
Lightweight Evaluation and Architecture Prototyping for Big Data (LEAP4BD)
This is one of the algorithms that provides a systematic approach to select a NoSQL. This method has 4 steps:
Assess The System Context & Landscape
This entails determining the core data holdings of the application, their connections, as well as the most popular queries and access patterns and quantifying anticipated data and transaction growth and establishing needed performance.
Identify The Architecturally-Significant Requirements & Decision Criteria
This step focuses on scalability, performance, security, availability, and data consistency as well as engaging with stakeholders to characterize the requirements for the attributes of an application’s quality.
Evaluate Candidate Technologies Against Quality Attribute Decision Criteria
This process comprises choosing a small group of options (usually two to four) for validation through prototyping and testing as well as finding and assessing prospective technologies against the application’s data and quality attribute criteria.
Validate Architecture Decisions & Technology Selections Through Focused Prototyping
In this stage, targeted prototyping was done, along with go/no-go criteria, and the behavior of the prototype was assessed using a set of carefully created, application-specific criteria.
Conclusion
To sum up, big data is the solution to most of the problems regarding managing the data in the traditional method since terabytes of data needs to be processed everyday. Even after adopting big data, it comes with its own challenges, Big Data Management Challenges. These challenges can be overcome by following the right steps and adopting the solutions.
