Big Data and Splunk

What is Big Data, why it is called so?

The term Big Data refers to those data sets, the size of which is beyond the capabilities of current database technology. Big data is extremely large and diverse collections of structured, unstructured, and semi-structured data that continues to grow exponentially over time. These datasets are so huge and complex in volume, velocity, and variety, that traditional data management systems cannot store, process, and analyze them.

So, what is the difference between these three data types?

Structured data:?

Structured data is well organized. It has a standardized format for efficient access by software as well as humans. It is typically tabular with rows and columns that clearly define data attributes. Computers can effectively process structured data for insights due to its quantitative nature. There are numerous examples of structured data from machines, such as POS data like quantity, barcodes. Similarly, data analysis using spreadsheets is a classic case of structured data generated by humans. Due to the organization of structured data, it is easier to analyze than both semi-structured and unstructured data.

Semi-structured data (partially structured data):

Semi-structured data refers to data that is not captured or formatted in conventional ways. It does not follow the format of a tabular data model or relational databases because it does not have a fixed schema. It is another category between structured and unstructured data. Semi-structured data is a type of data that has some consistent and definite characteristics. However, it does not confine into a rigid structure such as that needed for relational databases. Businesses use organizational properties like metadata or semantics tags with semi-structured data to make it more manageable. However, it still contains some variability and inconsistency.

Unstructured data:

It is a data in its absolutely raw format. This data is difficult to process due to its complex arrangement and formatting. Unstructured data includes social media posts, chats, satellite imagery, IoT sensor data, emails, and presentations. Unstructured data management takes this data to organize it in a logical, predefined manner in data storage. Natural language processing (NLP) tools help understand unstructured data that exists in a written format.

Why and how such a big data is generated?

In the international marketplace, businesses, suppliers, and customers create and consume vast amounts of information. Businesses and government agencies aggregate data from numerous private and/or public data sources. Private data is information that any organization exclusively stores that is available only to that organization, such as employee data, customer data, and machine data (e.g. user transactions and customer behavior). Public data is information that is available to the public for a fee or free, such as credit ratings, social media content (e.g., LinkedIn, Facebook, and Twitter).?

Big Data has now reached every sector in the world economy. It is transforming competitive opportunities in every industry sector including banking, healthcare, insurance, manufacturing, retail, wholesale, transportation, communications, construction, education, and utilities. It also plays key roles in trade operations such as marketing, operations, supply chain, and new business models.

The world’s volume of data doubles every 18 months. Digital information is doubling every 1.5 years. This deluge of data obviously creates a challenge to the business community and data scientists.??

Another interesting fact about Big Data is that not everything that is considered “Big Data” is in fact Big Data. One needs to explore deep into the scientific aspects, such as analyzing, processing, and storing huge volumes of data. That is the only way of using tools effectively. Data developers/scientists need to know about analytical processes, statistics, and machine learning. They also need to know how to use specific data to program algorithms. The core is the analytical side, but they also need the scientific background and in-depth technical knowledge of the tools they work with in order to gain control of huge volumes of data.

Let us now understand how Big Data and AI are related?

Big data and artificial intelligence have a synergistic relationship. AI requires a massive scale of data to learn and improve decision-making processes and big data analytics leverages AI for better data analysis. Big data thus helps in training the AI Models. Machine learning is a subset of AI. It relies heavily on the data used for training. Big Data provides the vast datasets needed to train AI models effectively. These models learn from historical data patterns, enabling them to make predictions or decisions based on new, unseen data. On the contrary, Artificial Intelligence can also help in managing big data. It involves using AI-driven algorithms and machine learning techniques to analyze, interpret, and derive actionable insights from large and complex datasets. The primary goal of AI in Big Data is to automate and enhance the process of data analysis, making it faster, more accurate, and scalable. AI helps in data management in numerous ways. AI can help retrieve lost data faster and more accurately. It can help in Data governance and compliance. It can detect and manage sensitive data to comply with GDPR and HIPAA. Data retention policies and audit trails can benefit. For Data Analytics, scalable AI-driven analytics tools can uncover insights and trends in datasets.

Let us understand it by simple example. A consumer's behavior and activities on internet while searching for his desirable products, and his past purchase history on various sites such as Google, Amazon, Flipkart, Meesho, Reliance, Tata, Big Bazaar is collected and analyzed. Accordingly AI generates advertisements for the particular customer when right product is available in market which suits the needs of that particular buyer.

We will now see what is the connection between Database, Data Wearhouse and Big Data?

A data warehouse is a set of software and techniques that facilitate data collection and integration into a centralized database. So one would wonder whether data warehouse just a big database? The basic difference is that a database stores the current data required to power an application whereas a data warehouse stores current and historical data for one or more systems in a predefined and fixed schema for the purpose of analyzing the data. As evident from the important differences between big data and data warehouse, they are not the same and therefore not interchangeable. Therefore big data solution will not replace data warehouse. So, is there something available which is having bigger capability than Data Wearhouse? Yes, it is data lake. While data warehouses store structured data, a lake is a centralized repository that allows you to store any data at any scale. A data lake offers more storage options, has more complexity, and has different use cases compared to a data warehouse.

There are 5Vs of Big Data:

Big data is described by five characteristics which are called a s5 Vs of big data:?

volume, value, variety, velocity, and veracity.

Volume: It talks about the size and amounts of big data that companies manage and analyze. Keywords: Terabytes, records, architecture, files, tables and their distribution.

Value: It is the most important for the businesses. The value comes from insight discovery and pattern recognition that lead to more effective operations, stronger customer relationships and quantifiable business benefits. Keywords: Statistical, events, co-relation, and hypothetical.

Variety: The diversity and range of different data types, including unstructured data, semi-structured data and raw data. Keywords: Structured, Unstructured, multi-factor, probabilistic, linked and dynamic.

Velocity: the speed at which companies receive, store and manage data, for example the specific number of social media posts or search queries received within a day, hour or minute etc. Keywords: Batch, Process, Stream, Real time, Near real time.

Veracity: It is the “truth” or accuracy of data and information assets. Keywords: Trustworthiness, Authenticity, Origin, Reputation, Availability, Accountability.?

The Big data field is vast and everything can not be explained in the article.?

We will now see what is Splunk?

Splunk is a big data platform that simplifies the task of collecting and managing massive volumes of machine-generated data and searching for information within it. The technology is used for business and web analytics, application management, compliance, and security.

Splunk makes machine data accessible, usable & valuable. Splunk indexes the data, searches and reports, adds knowledge to it, reports and analyses and monitors and alerts which generates operational intelligence, log analytics and machine data visualizations.?

Splunk is an advanced and scalable form of software that indexes and searches for log files within a system and analyzes data for operational intelligence. The software is responsible for splunking data, which means it correlates, captures, and indexes real-time data, from which it creates alerts, dashboards, graphs, reports, and visualizations. This helps organizations recognize common data patterns, diagnose potential problems, apply intelligence to business operations, and produce metrics.

Splunk’s software can be used to examine, monitor, and search for machine-generated big data through a browser-like interface. It makes searching for a particular piece of data quick and easy, and more importantly, does not require a database to store data as it uses indexes for storage.?

Key benefits of Splunk include:

Enhanced GUI and Real-time Visibility: Splunk provides an intuitive graphical user interface (GUI) and real-time visibility through dashboards. This allows users to monitor and analyze data efficiently, leading to quicker insights and decision-making.

Reduced Downtime: With Splunk, organizations can streamline workflows, standardize processes, and respond faster to incidents. By detecting and addressing issues promptly, Splunk helps prevent major disruptions and reduces downtime. This results in happier customers and more reliable services.

Quicker Mean Time to Remediation: When unexpected situations arise, Splunk enables agile responses. Whether rolling out new code, updating legacy infrastructure, or exploring new business models, organizations can pivot confidently with full visibility into the impact of changes on their digital environment.

Root Cause Analysis: Splunk is well-suited for root cause analysis. It reduces troubleshooting and resolution time by providing instant results, making it easier for organizations to identify the underlying causes of issues.

Centralized Data View: By collecting and indexing data from across an organization, Splunk offers a centralized view of all critical data. This helps improve operations, make better decisions, and reduce costs.

Compliance and Reporting: Splunk simplifies compliance management and reporting. Organizations can easily track and analyze data related to regulatory requirements and internal policies.

Full Visibility into IT and Business Operations: Splunk’s capabilities extend beyond IT operations. It provides insights into business processes, customer behavior, and other areas where large volumes of data are involved.

Splunk empowers organizations: It enhances resilience, respond swiftly to incidents, and gain valuable insights from their data. Whether it’s monitoring web traffic during holiday seasons or adapting to changing business needs, Splunk plays a crucial role in modern enterprises.

What is ETL?

ETL is a process that extracts, transforms and loads data from multiple sources to a data warehouse or other unified data repository.

Is Splunk an ETL tool?

While Splunk is not a traditional Extract, Transform, Load (ETL) tool, it can be used for some ETL-like tasks.?

Extract:

Splunk excels at extracting data from various sources, including logs, files, databases, APIs, and more. It ingests data in real-time or batch mode, making it suitable for collecting raw data from diverse systems.

Transform:

Although Splunk doesn’t have built-in ETL transformations like dedicated ETL tools, it offers powerful search and query capabilities. You can use Splunk’s search processing language (SPL) to manipulate and transform data during analysis. SPL allows you to filter, aggregate, join, and perform calculations on data. For example, you can extract fields, create new ones, and apply statistical functions—all within Splunk.

Load:

Splunk stores ingested data in its proprietary index format. While it doesn’t directly load data into traditional data warehouses or databases, it provides storage and indexing for efficient querying. If you need to move data to other systems, you can export results from Splunk searches to external destinations (e.g., CSV files, databases, or APIs). However, this step is not as seamless as a dedicated ETL process.

ETL-like use cases with Splunk:

Data Enrichment:?

Splunk can enrich raw data by adding context (e.g., geolocation, user information) using lookups or external data sources.

Data Normalization:?

You can standardize and normalize data formats within Splunk.

Alerting and Workflow Automation: Splunk can trigger alerts based on specific conditions, which can be considered a form of ETL-driven action.

Custom Scripts and Add-ons:?

Splunk allows custom scripts and add-ons to extend its functionality, enabling ETL-like processes.

Considerations:

While Splunk can handle some ETL tasks, it’s not a replacement for dedicated ETL tools in complex data integration scenarios.

For large-scale ETL pipelines, organizations often use specialized ETL tools (e.g., Apache NiFi, Talend, Informatica) that offer more features, scalability, and orchestration capabilities.

In summary, while Splunk isn’t a traditional ETL tool, it can serve ETL-like purposes within its domain of log analysis, real-time monitoring, and data exploration. Organizations should evaluate their specific requirements and choose the right tools accordingly.

How Splunk is used for Security?

Splunk Enterprise Security (Splunk ES) is a security information and event management (SIEM) solution that collects data from all security tools and IT systems, enabling security teams to detect, triage, and respond to security incidents. It provides simplified threat management that facilitates quick threat detection and response and minimizes risk. Splunk ES can help you achieve continuous monitoring, support your security operations center (SOC), implement incident response, or inform stakeholders about business risks.

8 Splunk Security Solutions:

1. Splunk Security Cloud: Splunk Security Cloud is a security information and event management (SIEM) solution offered as a managed cloud service.?

2. Splunk SOAR: Splunk SOAR can improve productivity for security analysts and reduce response time to security incidents, with the capabilities like Automates repetitive tasks, Automatically detects and triages security incidents, Orchestrates complex workflows across teams and tools, Event and case management, Integrated threat intelligence and Reporting and collaboration.

3. Splunk Enterprise Security: Splunk Enterprise Security (Splunk ES) is a security information and event management (SIEM) solution that collects data from all security tools and IT systems, enabling security teams to detect, triage, and respond to security incidents. It powers the Splunk Security Cloud service, and can also be deployed on-premises as a standalone application.

4. Splunk Infrastructure Monitoring: Splunk Infrastructure Monitoring auto-discovers the IT stack and integrates with hundreds of platforms and solutions to ingest operational data. It supports hybrid cloud and multi-cloud, and enables real-time monitoring of large scale environments.

5. Splunk Mission Control: Splunk Mission Control is a platform that enables management of security operations efforts. It is a SaaS-based solution that lets security teams detect, manage, hunt, and mitigate threats from one interface. It is fully integrated with Splunk Enterprise Security.

6. Splunk Application Performance Monitoring (Splunk APM): Splunk APM provides performance monitoring and troubleshooting for cloud native applications. It provides high fidelity tracing based on 100% of data, and enables real-time alerting via the Splunk streaming architecture.

7. Splunk IT Service Intelligence (Splunk ITSI): Splunk IT Service Intelligence (ITSI) collects data from IT services and predicts incidents before they happen. It performs real-time predictive analytics on operational data based on machine learning.

8. Splunk User Behavior Analytics (UBA): Splunk UBA can discover hidden threats by establishing behavioral baselines for users, devices, and applications, and identifying anomalies even if they don’t meet any known threat pattern.

Does Splunk have competitors?

Other important factors to consider when researching alternatives to Splunk Enterprise include search and dashboards. We have compiled a list of solutions that reviewers voted as the best overall alternatives and competitors to Splunk Enterprise, including Sematext, Datadog, Mezmo, Loggly, Sumo Logic, Dynatrace, Elastic Stack, New Relic, Graylog, AppDynamics etc.

How is Splunk better than other tools?

Splunk provides a wide range of tools for analyzing and visualizing your data fast and at scale. This way, you identify patterns, detect anomalies and make informed decisions. At its core, Splunk provides capabilities such as: Unified security and observability.