Data is expanding, and so are the enterprise challenges to manage this growth. Most of these challenges boil down to the inability to accommodate the dynamic influx of data. Even the good old ETL is underperforming, resulting in 70% of failed initiatives, calling for reconsidering the data integration practices.  But what went wrong?

In the traditional setup, the ETL captures data from multiple sources and schedules it into batches.

These data sets in the batches are then further extracted (E), transformed (T), and finally loaded (L) into the targeted system. Since the technique involves bulk processing and periodic updates, it causes delays in overall processing and the time to expected outcome. Ultimately, businesses are deprived of real-time insights.

Modern techniques such as ELT can capture real-time data by processing only the incremental changes from the last extraction. It enables organizations to utilize their resources optimally and focus on more resource-conscious data integration. It reduces latency and provides on-demand access to updated data, making prompt decisions.

Due to its outdated tech, ETL cannot put such vast amounts of real-time data into efficient utilization. It might have been the rockstar of the past decade, but the modern, rapid Web3 landscape asks for a lot more.

What are the different ways in which traditional ETL might be hindering your business growth?

Given the exponential increase in data volume, traditional ETL pipelines struggle to accommodate the rush, causing slower processing and uncertain increase in the cost. Not only does it suppress an organization’s ability to leverage data insights, but it almost nullifies the opportunity to innovate.

Moreover, the complex schema in traditional systems requires significant upgrading and maintenance efforts. Over a period of time, as businesses scale, the increase in sources makes it difficult for the pipeline to stay up to date.

Additionally, traditional ETL’s dependency on centralized processing invites single points of failure, compromising data integrity and system security.

What to do? Move from outdated tech and embrace modern alternatives to ETL.

To match the expectations of the impatient consumer, It is imperative for organizations, regardless of their scale, to break free and unlock optimal value from data. Using modern ETL tools, businesses can streamline the complex data landscape, something ETLs have been trying to do for a long time.

ELTs follow the Load-first approach wherein the raw data is initially loaded into the target system, and transformation occurs in-database or using distributed processing frameworks.

Change Data Capture (CDC), for example, processes high volumes of data as soon as it enters the framework, thereby actualizing real-time insights at the other end. Likewise, cloud-based solutions provide scalable and cost-effective data processing, ensuring enterprises can adapt and grow without hardware limitations. Wait for the next section, where I explain a detailed case study.

Data lakes and hubs allow enterprises to store and process vast amounts of raw data from multiple sources. This approach fosters democratization and enables cross-functional teams to analyze data.

A quick case study to understand the impact of modern ETL

A major telecom company operating amidst a high volume of real-time data struggled to manage the influx with traditional ETL systems. At one point, it almost gave up on improving its network performance largely because the data was scattered across multiple sources. Such an inefficiency hindered the timely responses to customers who had become impatient during the lockdown.

To move on to newer alternatives, the company implemented Skyvia, a cloud ETL platform that integrates data from multiple sources into a unified warehouse. Now, all data strategically stored and accessible at one point facilitated significant improvements in understanding the network’s health. Post implementation, it helped the organization achieve remarkable improvements in network performance, thereby reducing the outages by 50% and boosting the average speed of the network by 20%.

Furthermore, this led to significant cost savings and enhanced customer delight. With a reduction in data integration time from a week to a single day, the business could respond at the moment to all critical situations, escalations, and other ad-hoc events.

Ultimately, the company recorded a 10% enhancement in CX ratings, reclaiming its lost reputation.

Today, the telecom company is future-ready to thrive in the highly competitive market. Moving from outdated ETL practices to contemporary cloud-based solutions has led to significant growth and loyalty. Skyvia’s cloud-based data integration solution is a reminder for businesses that are tapping upon scalability and flexibility isn’t a tough game until they sign up for similar transformations. As we know, a SaaS landscape offers pricing models as per your consumption, drastically abbreviating upfront costs.

Make your enterprise future-ready

The traditional ETL approach stands as a barrier to innovation for modern enterprises. The inherent limitations of batch processing and complete data extraction no longer align with the demands of real-time decision-making and dynamic market landscapes.

As discussed, the inherent limitations of batch processing no longer suffice the in-the-moment decision-making expectations. Businesses must take a leap, embrace agile approaches, and extract true value from the data mountain. It’s no longer a choice but a strategic imperative.

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With the realization of what data can do in catering to users in providing a unique experience of a product or service, businesses are collating data from all sources. The collected data is huge in volume and is shared with many stakeholders to derive meaningful insights or to serve the customers.

This data sharing results in regular data breach occurrences that affect companies of all sizes and in every industry — exposing the sensitive data of millions of people every year and costing businesses millions of dollars. According to an IBM report, the average cost of a data breach in 2022 is $4.35 million, up from $4.24 million in 2021. It becomes imperative to secure access to sensitive data that flows across an organization for faster development, service, and production at scale without compromising its privacy.

Data masking anonymizes and conceals sensitive data

Data masking anonymizes or conceals this sensitive data while allowing it to be leveraged for various purposes or within different environments.

Create an alternate version in the same format as of data

The data masking technique protects data by creating an alternate version in the same format as of data. The alternate version is functional but cannot be decoded or reverse-engineered. The modified version of the original data is consistent across multiple Databases. It is used to protect different types of data.

Common data types (Sensitive data) for Data Masking

• PII: Personally Identifiable Information
• PHI: Protected Health Information
• PCI-DSS: Payment Card Industry Data Security Standard
• ITAR: Intellectual Property Information

According to a study by Mordor Intelligence, “The Data Masking Market” was valued at USD 483.90 million in 2020 and is expected to reach USD 1044.93 million by 2026, at a CAGR of 13.69% over the forecast period 2021 — 2026.

In this information age, cyber security is very important.” Data masking helps secure this sensitive data by providing a masked version of the real-time data while preserving its business value (see: k2view dotcom; “what is data masking”). It also addresses threats, including Data Loss, Data Exfiltration, insider threats or account breach, etc.

Many data masking techniques are used to create a non-identifiable or undeciphered version of sensitive data to prevent any data leaks. It maintains data confidentiality and helps businesses to comply with data security standards such as General Data Protection Regulation (GDPR), Payment Card Industry Data Security Standard (PCI DSS), etc.

Common Methods of Data Masking

1.  Static Data Masking

This method of data masking is very commonly used to mask data in a production environment. In this method, the hidden data retains its original structure without revealing the actual information. The data is altered to make it look accurate and close to its original characteristics so that it can be leveraged in development, testing, or training environments.

2.  Dynamic data masking

This method is different from static masking in a way that active or live data is masked without altering the original data form. Thus, in this method, the data is masked only at a particular database layer to prevent unauthorized access to the information in different environments.

With this method, organizations can conceal data dynamically while managing data requests from third-party vendors, parties, or internal stakeholders. It is used to process customer inquiries around payments or handle medical records within applications or websites.

Informatica offers PowerCenter with PowerExchange for Extract Transform Load (ETL) and ILM for data masking. These products embody best practices for handling large datasets across multiple technologies and sources.

Informatica Dynamic Data Masking anonymizes data and manages unauthorized access to sensitive information in production environments, such as customer service, billing, order management, and customer engagement. Informatica PowerCenter Data Masking Option transforms production data into real-looking anonymized data.

3.  On-the-fly data masking

The on-the-fly data masking method is considered ideal for organizations that integrate data continuously. With this method, the data is masked when transferred from a production environment to another environment, such as a development or test. A portion of data or smaller subsets of data is masked, as required, thus eliminating the need to create a continuous copy of masked data in a staging environment, which is used to prepare data.

Different platforms use each or a combination of these methods to implement data masking. For example, K2view offers data masking through the data product platform that simplifies the data masking process of all the data related to specific business entities, such as customers, orders, credit card numbers, etc.

The K2view platform manages the integration and delivery of this sensitive data of each business entity masked in its encrypted Micro-Database. It uses dynamic data masking methods for operational services like customer data management (customer 360) or Test data (test data management), etc.

Another example of using both static and dynamic data masking methods is Baffle Data Protection Services (DPS). It helps to mitigate the risks of data leakage from different types of data, such as PII, Test data across a variety of sources. With Baffle, businesses can build their own Data Protection Service layer to store personal data at the source and manage strong access controls at that source with Adaptive Data Security.

Popular Data Masking Techniques

• Data Encryption

Data Encryption is the most common and reliable data-securing technique. This technique hides data that needs to be restored to its original value when required. The encryption method conceals the data and decrypts it using an encryption key. Production data or data in motion can be secured using data encryption technology, as the data access can be limited to only authorized individuals and can be restored as required.

• Data Scrambling

The Data Scrambling technique secures some types of data by rearranging the original data with characters or numbers in random order. In this technique, once the data is scrambled with random content, the original data cannot be restored. It is a relatively simple technique, but the limitation lies with only particular types of data and less security. Any data undergoing scrambling is viewed differently (with randomized characters or numbers) in different environments.

• Nulling Out

The Nulling Out technique assigns a null value to sensitive data in order to bring anonymity to the data to protect data from unauthorized usage. In this technique, the null value in place of original information changes the characteristics of data and affects the usefulness of data. The method of removing data or replacing data with a null value takes away its usefulness — making it unfit for test or development environments. Data integration becomes a challenge with this type of data manipulation, which is replaced with empty or null values.

• Shuffling

The shuffling data technique makes the hidden data look authentic by shuffling the same column values that are shuffled randomly to reorder the values. For instance, this technique is often used to shuffle employee names columns of records such as Salaries; or, in the case of patient names, columns shuffled across multiple patient records.

The shuffled data appear accurate but do not give away any sensitive information. The technique is popular for large datasets.

• Data Redaction (blacklining)

The Data Redaction technique, also known as blacklining, does not retain the attributes of the original data and masks data with generic values. This technique is similar to nulling out and is used when sensitive data in its complete and original state is not required for development or testing purposes.

For instance, the replacement of credit card number with x’s (xxxx xxxx xxxx 1234) displayed on payment pages in the online environment helps to prevent any data leak. At the same time, the replacement of digits by x helps developers to understand what the data might look like in real-time.

• Substitution

The Substitution technique is considered to be the most effective for preserving the data’s original structure, and it can be used with a variety of data types. The data is masked by substituting it with another value to alter its meaning.

For example, in the customer records substituting the first name ‘X’ with ‘Y’ retains the structure of the data and makes it appear to be a valid data entry, yet provides protection against accidental disclosure of the actual values.

Conclusion

Data masking has emerged as a necessary step for transforming real-time data to non-production environments while maintaining the security and privacy of sensitive data.

Masking of data is crucial when managing large volumes of data and gives the authorization to dictate the access of data in the best possible way.

Featured Image Credit: Provided by the Author; Pexels; Thank you!

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Enterprises are aggressively investigating beyond the capabilities of traditional data integration such as Extract Transform Load (ETL) systems or data warehouse software as they acquire large volumes of diverse data from an increasing number of sources. Here is a comprehensive guide to data virtualization for enterprises.

Businesses are deploying data virtualization technology solutions to meet increasing data demand for multiple purposes ranging from faster provisioning of new data to enabling self-service data access to clients. It is proving tremendously helpful to data consumers, IT, and technical teams.

Data Virtualization is a Mature Technology

Data virtualization is a mature technology currently used as a part of a company’s data integration strategy. According to MarketsandMarkets, the data virtualization market size is expected to grow to USD 1.58 billion in 2017. Furthermore, it is projected to reach USD 4.12 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 21.1% during the forecast period (2017 to 2022).

Data Virtualization Technology Creates a Logical Extraction Layer

Data virtualization technology creates a logical extraction layer in distributed data management processing. It allows users to access data of any format and heterogeneous source (data warehouse or data lake) in a standardized manner.

As a result, the users of the data do not need to deal with the technical aspects of data, such as where and how the data is stored, the type of data and its storage structure, and the interface of the original source of data storage, etc.

Further, this data is consumed through virtual views by applications, query/reporting tools, message-oriented middleware, or other data management infrastructure components.

How Does Data Virtualization Work for an Enterprise?

Enterprises can easily access the data they require with data virtualization. A three-step process is involved in the implementation of data virtualization:

Connect: Data virtualization connects to varied data sources, i.e., databases, data warehouses, cloud applications, big data repositories, and even Excel files.

Combine: Data virtualization combines and transforms the related information or date of any format into business views or insights.

Deliver: Data virtualization accesses and delivers real-time data through reports, dashboards, portals, mobile apps, and Web applications to enterprises

While data virtualization technology combines various data sources in a single user interface, the virtual or semantic layer is at the heart of the technology. It allows data or business users to organize their data in different virtual schemas further and virtual views in any format and from any source.

Users can access all unified data from diverse systems through the virtual layer, which produces a single consolidated data source. This information is safe and secure and complies with all industry requirements.

Users can easily enhance this virtualized data to prepare it for analytics, reporting, and automation procedures.

Why Do You Need to Virtualize Data?

These factors drive data virtualization’s growing importance:-

Meets data Demands: As enterprises continue to undertake analysis and employ self-service analytics tools, the data demands of business and data analysts, scientists, and engineers on board might become unmanageable. The findings aid businesses in making better decisions and delighting their customers. As a result, data virtualization allows you to view all your data in real-time from a single, centralized location. This enables the completion of analytics faster than usual.

Manages Data complexity and Volume: The quest for fast expansion has increased the number of unconnected physical databases and complex data in businesses. The quickest way to combine them for analytics is to use data virtualization.

The pace of data generation is clearly increasing, making it more challenging to keep a physical data warehouse up to date. In addition, data virtualization is a more advanced method of transferring data from several locations.

Provides Data Agility: While giving business users a self-service option may be a priority, enterprises constantly strive to strike the right balance between strong security and business agility. Data virtualization makes all enterprise data accessible to different users and uses cases through a single virtual layer. In addition, prototyping capabilities are built into data virtualization technology, allowing users to test the strategy in real-time before deploying it on a larger scale.

Provides Secure Governance: As the volume, variety, and complexity of data rises, compliance, data asset protection, and risk mitigation become more critical aspects of every data management strategy.

Data virtualization establishes access rules for who should have access to what data, making the data secure for usage. In addition, it enables security management, data governance, and performance monitoring by providing a centralized point of access to all types of information in the company.

Popular Data Virtualization Tools

Enterprises have been collecting data from numerous destinations or sources into data warehouses, data, or data lakes to consolidate it for further analysis and decision making.

As discussed, with increasing volume and variation of data, the data integration process seems time-consuming, costly, and prone to errors. Thus, many businesses use data virtualization software because it lets them view, access, and analyze data without worrying about the data lifecycle. Here are some popular tools to consider:

TIBCO

TIBCO Software is well-known for its data and analytics software, but it also offers an increasing number of integration options. For example, TIBCO data virtualization allows you to access various data sources. In addition, the tool includes an orchestrated data layer, centralized metadata management, and powerful query options like Advanced Query Engines that aid in data delivery on demand.

The studio design tool, service UI, and business data directory are some of the essential features, which empower users to search for and pick virtualized business data from a self-service directory, and then analyze the findings using their preferred analytics tools. With the help of Web Services Description Language, abstracted data can be made available as a data service in TIBCO. The built-in governance and security ensure that sanctioned data is also delivered to users.

K2View

K2View is a significant figure among vendors in the market. It offers Dynamic Data Virtualization technology for agile data integration by removing the difficulty of accessing data from various underlying data sources, formats, and structures.

Its capabilities range from ingesting data from any source, unifying it via a semantic layer, possibly storing it (physically or in memory), processing it, and eventually making it available to data analysts and consuming applications.

To offer access to real data, this tool uses a logical abstraction layer called data product schema. This schema unifies the information for a specific business entity by bringing together all tables and fields.

It allows you to virtualize or store data with ease. For example, instead of virtualizing data that is not highly dynamic, businesses can choose to keep it. It also allows for smooth data access via any technique, such as SQL or web service APIs, or data delivery (“pushing”) to data consumers via data streaming or messaging protocols.

Denodo

Denodo offers enterprise-grade data virtualization capabilities with an easy-to-use interface. In addition, it includes a data catalog feature that makes data search and discovery easier. This tool can be used on-premises, in the cloud, or a hybrid environment.

Key capabilities include query optimization feature, which improves query performance and reduces response times. In addition, it delivers integrated data governance solutions for enterprises concerned about data protection and compliance.

This tool includes an active data catalog for semantic search and data governance, AI-powered smart query acceleration, automated cloud infrastructure management for multi-cloud and hybrid deployments, and embedded data preparation capabilities for self-service yet well-governed and secure analytics.

Denodo also provides unified enterprise data access, business intelligence, data analytics, and single-view apps.

Conclusion

With the increasing complexity of corporate operations, businesses continue to use various data management solutions. As a result, the data architecture is becoming increasingly intricate.

As a middleware enabling a company to manage data across on-premises, cloud, or hybrid infrastructure, data virtualization is relatively simpler to establish. It enables real-time synchronization of disparate data sources without requiring data replication, lowering infrastructure costs.

Your data engineering team will be able to create clear and concise data views using data virtualization software’s comprehensive analytics, design, and development features. Data virtualization software will enable your data engineering team to design clean and concise data views using rich analytics, design, and development features.

Furthermore, selecting the best data virtualization tool and solution necessitates a thorough examination of their technological capabilities.

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