In a file processing system, duplicated data can increase the chance of errors.

Although most companies benefit from the extensive use of such information resources at their workers' hands, some face problems with the accuracy of the data they use.

As most organizations nowadays also look at implementing artificial intelligence systems or connecting their business via the Internet of Things, this becomes especially important.

Data quality issues can stem from duplicate data, unstructured data, incomplete data, different data formats, or the difficulty accessing the data. In this article, we will discuss the most common quality issues with data and how to overcome these. 

Duplicate data

Multiple copies of the same records take a toll on computing and storing, but may also produce skewed or incorrect insights when undetected. One of the critical problems could be human error — someone simply entering data multiple times by accident — or an algorithm that went wrong. 

The solution suggested for this problem is called "data deduplication." It is a combination of human intuition, data analysis, and algorithms to detect possible duplicates based on chance scores and common sense to determine where records look like a near match.

Unstructured data

Many times, if data has not been entered correctly in the system, or some files may have been corrupted, the remaining data has many missing variables. For example, if the address does not contain a zipcode at all, the remaining details might be of little interest, because it will be challenging to determine the geographical dimension.

With a data integration tool, you can help convert unstructured data to structured data. And also, move data from various formats into one consistent form.

Security issues

In addition to industry and regulatory standards such as HIPAA or PCI Data Security Standards (PCI DSS), data security and compliance requirements come from different sources and include organizational requirements. Failure to comply with these rules can result in hefty fines and, perhaps, even more expensive, loss of customer loyalty. Guidelines provided by regulations such as HIPAA and PCI also present a compelling argument for a robust data quality management system.

Consolidating the management of privacy and security enforcement as part of an overall data governance program gives a significant advantage. This may include integrated data management and auditor-validated data quality control procedures, giving business leaders and IT confidence that their company meets critical privacy requirements and protections against possible data leaks. By protecting customer data integrity with a unified data quality program, customers are encouraged to build strong and lasting connections to the brand.

Hidden data

Most companies are using only about 20% of their data when making business intelligence decisions, leaving 80% to sit in a metaphorical dumpster. Hidden data are most beneficial in regards to customer behavior. Customers interact with companies today in a variety of mediums, from in-person to over the phone to online. Data can be invaluable on when, how, and why customers interface with a company, but it is rarely utilized.

Capturing hidden data with a tool like the Datumize Data Collector (DDC) can give many more insights into the hidden data you now have obtained.  

Inaccurate data

Finally, there's no point in running big data analytics or making contact with customers based on data that is just plain wrong. Data can quickly become inaccurate. By not gathering all the hidden data, your data is not complete and limits you from making decisions based on complete and accurate data sets. The more obvious way for inaccurate data is data in systems filled with human mistakes, like a type or wrong information provided by the customer or inputting details in the wrong field. 

These can be among the toughest data quality issues to be found, mainly if the encoding is still appropriate- for example, entering an inaccurate, but legitimate, social security number can go unnoticed by a database that only checks in isolation the veracity of the information.

Data Deduplication, also known as Intelligent Compression or Single-Instance Storage, is a method of reducing storage overhead by eliminating redundant copies of data. Data Deduplication techniques ensure that on storage media such as discs, flash, tape, etc., only one unique instance of data is kept. A pointer to the unique data copy replaces redundant data blocks. Data Deduplication therefore closely resembles incremental backup, which transfers just the data that has changed since the last backup.

Here’s all you need to know about Data Deduplication, as well as some key pointers to keep in mind before you start the process.

Table of Contents

What is Data Deduplication?

Data Deduplication, or Dedup for short, is a technology that can help lower the cost of storage by reducing the impact of redundant data. Data Deduplication, when enabled, maximizes free space on a volume by reviewing the data on the volume and looking for duplicated portions. Duplicated portions of the dataset of a volume are only stored once and (optionally) compacted to save even more space. Data Deduplication reduces redundancy while maintaining Data Integrity and Veracity.

How does Data Deduplication Work?

Data Deduplication eliminates duplicate data blocks and stores only unique data blocks at the 4KB block level within a FlexVol volume and across all volumes in the aggregate. Data Deduplication relies on fingerprints, which are unique digital signatures for all 4KB data blocks. The Inline Deduplication Engine examines the incoming blocks, develops a fingerprint, and stores the fingerprint in a hash store when data is written to the system (in-memory data structure). A lookup in the hash store is conducted once the fingerprint is calculated. The data block matching to the Duplicate Fingerprint (Donor Block) is examined in cache memory when a fingerprint match is found in the hash store:

• If a match is detected, a byte-by-byte comparison is performed between the current data block (receiver block) and the donor block as verification. The recipient block is shared with the matching donor block during verification without the recipient block being written to the disc. To keep track of the sharing details, just the metadata is updated.
• If the donor block is not found in cache memory, it is prefetched from the disc into the cache and compared byte-by-byte to ensure an exact match. Without actually writing to the disc, the recipient block is flagged as a duplicate during verification. To keep track of sharing details, metadata is updated.

In the same way, the background deduplication engine functions. It searches all of the data blocks in bulk and removes duplicates by comparing block fingerprints and performing a byte-by-byte comparison to eliminate false positives. This method also assures that no data is lost during the Deduplication process.

What is the Importance of Deduplicating Data?

• Data Deduplication is crucial because it decreases your storage space requirements, saving you money and reducing the amount of bandwidth used to move data to and from remote storage sites. 
• Data Deduplication can reduce storage requirements by up to 95% in some circumstances, while your specific Deduplication Ratio can be influenced by factors such as the type of data you’re attempting to deduplicate. 
• Even if your storage requirements are decreased by less than 95%, Data Deduplication can save you a lot of money and boost your bandwidth availability significantly.

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What are the Data Deduplication Ratios & Key Terms?

A Data Deduplication Ratio is the comparison of the data’s original size and its size after redundancy is removed, as previously stated. It’s basically a metric for how effective the Deduplication procedure is. Given that much of the redundancy has already been eliminated, the Deduplication procedure produces comparatively poorer outcomes as the Deduplication ratio rises. A 500:1 Deduplication Ratio, for example, is not much better than a 100:1 Deduplication Ratio—in the former, 99.8% of data is deleted, whereas, in the latter, 99% of data is eliminated.

The following factors have the greatest impact on the Deduplication Ratio:

• Data Retention: The longer data is kept, the more likely it is that redundancy will be discovered.
• Data Type: Certain types of files are more prone than others to contain a high level of redundancy.
• Change Rate: If your data changes frequently, your Deduplication Ratio will most likely be lower.
• Location: The broader the scope of your Data Deduplication operations, the more likely duplicates may be discovered. Global deduplication over numerous systems, for example, usually produces a greater ratio than local Deduplication on a single device.

Data Deduplication Use Cases: Where can it be useful? 

General Purpose File Servers

General-purpose File Servers are file servers that are used for a variety of purposes and may hold any of the following sorts of shares:

• Shared by the entire group
• Home folders for users
• Folders for work
• Shares in software development

Since multiple users tend to have many copies or revisions of the same file, general-purpose file servers are a strong choice for Data Deduplication. Since many binaries remain substantially unchanged from build to build, Data Deduplication benefits software development shares.

Virtual Desktop Infrastructure (VDI) deployments

Virtual Desktop Infrastructure (VDI) Deployments: VDI servers, such as Remote Desktop Services, offer a lightweight way for businesses to supply PCs to their employees. There are numerous reasons for a company to use such technology:

• Application deployment: You can deploy applications throughout your entire organization rapidly. This is especially useful when dealing with apps that are regularly updated, rarely utilized, or difficult to administer.
• Application consolidation: It eliminates the requirement to update the software on client computers by installing and running them from a group of centrally controlled virtual machines. This option also minimizes the amount of bandwidth necessary to access programs on the network.
• Remote Access: Users can access enterprise programs via remote access from devices such as personal computers, kiosks, low-powered hardware, and operating systems other than Windows.
• Branch office access: VDI deployments can improve the performance of applications for branch office workers who need access to centralized data repositories. Client/server protocols for data-intensive applications aren’t always designed for low-speed connections.

Since the virtual hard discs that drive the remote desktops for users are virtually identical, VDI deployments are excellent candidates for Data Deduplication. Additionally, Data Deduplication can help with the so-called VDI boot storm, which is when a large number of users sign in to their desktops at the same time to start the day.

Backup Targets 

Virtualized backup apps, for example, are Backup Targets. Owing to the large duplication between backup snapshots, backup programs like Microsoft Data Protection Manager (DPM) are great candidates for Data Deduplication.

What are the Method/Types of Data Deduplication Approaches?

Inline Deduplication

When data is written to storage, Inline Deduplication occurs. The Deduplication Engine tags the data progressively while it is in motion. While this method is effective, it does result in additional computing overhead. The system must tag incoming data on a regular basis and then quickly determine whether or not that new fingerprint matches anything in the system. A flag pointing to the existing tag is written if this is the case. If it doesn’t, the block will be preserved as-is. Inline Deduplication is a common feature on many storage systems, and while it does add overhead, it’s not too bad because the benefits outweigh the costs.

Post-processing Deduplication

When all data is written completely, Post-Process Deduplication, also known as Asynchronous Deduplication, occurs. The Deduplication System runs through and tags all new data removes multiple copies and replaces them with flags pointing to the original data copy at regular intervals. Businesses can use their Data Reduction service without worrying about the repetitive processing overhead produced by Inline Deduplication when using Post-Process Deduplication. This method allows organizations to schedule Deduplication so that it can take place during non-business hours.

The most significant disadvantage of Post-Process Deduplication is that all data is stored in its entirety (often called fully hydrated). As a result, the data takes up the same amount of space as non-deduplicated data. Size reduction occurs only when the scheduled Deduplication operation has been completed. Businesses that use post-process dedupe must have a bigger storage capacity overhead at all times.

Source Deduplication

Before sending data to a backup target, Source-based Deduplication removes redundant blocks at the client or server level. There is no need for any additional gear. Deduplicating data at the source saves time and space.

Target Deduplication

Backups are sent via a network to disk-based hardware present in a remote location with Target-based Deduplication. Deduplication targets raise expenses, but they usually provide a performance advantage over source deduplication, especially for petabyte-scale data sets.

Client-side Deduplication

Client-side Data Deduplication is a Data Deduplication technique that is used on a backup-archive client. For example, to remove redundant data during backup and archive processing before the data is sent to the server. The amount of data delivered across a local area network can be reduced by using Client-side Data Deduplication.

What Makes Hevo’s ETL Process Best-In-Class

Providing a high-quality ETL solution can be a difficult task if you have a large volume of data. Hevo’s automated, No-code platform empowers you with everything you need to have for a smooth data replication experience.

Check out what makes Hevo amazing:

• Fully Managed: Hevo requires no management and maintenance as it is a fully automated platform.
• Data Transformation: Hevo provides a simple interface to perfect, modify, and enrich the data you want to transfer.
• Faster Insight Generation: Hevo offers near real-time data replication so you have access to real-time insight generation and faster decision-making. 
• Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
• Scalable Infrastructure: Hevo has in-built integrations for 100+ sources (with 40+ free sources) that can help you scale your data infrastructure as required.
• Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

Data Deduplication vs Thin Processing vs Compression: What is the Difference?

Compression is another approach frequently associated with Deduplication. Data dedupe, on the other hand, looks for duplicate data chunks, whereas compression utilizes a method to minimize the number of bits required to represent data. When data comes to Deduplication, Compression and Delta Differencing are frequently used. When combined, these three data reduction strategies are intended to maximize storage capacity.

In a storage area network, thin provisioning maximizes capacity use. Erasure coding, on the other hand, is a Data Protection Strategy that divides data into fragments and encodes each fragment with redundant data to aid in the reconstruction of corrupted data sets.

Deduplication also has the following advantages:

• A smaller data footprint; 
• less bandwidth used while copying data for remote backups, replication, and disaster recovery;
• Longer periods of retention;
• Reduced tape backups and faster recovery time targets

Block vs File-level Data Deduplication: What sets them apart?

Data Deduplication is often done on a file or block level. File Deduplication removes duplicate files, although it is not a very effective method of doing it. Data Deduplication at the file level compares a file that needs to be backed up or archived with copies that already exist. This is accomplished by comparing its attributes to an index. If the file is unique, it is saved and the index is changed; otherwise, only a link to the existing file is saved. As a result, only one copy of the file is saved, with subsequent copies being replaced with a stub pointing to the original.

Block-level Deduplication searches a file for unique iterations of each block and preserves them. All of the blocks are separated into pieces of the same length. A hash algorithm, such as MD5 or SHA-1, is used to process each block of data. This method assigns each component a unique number, which is then placed in an index.

Even if only a few bytes of the content or presentation have changed, when a file is updated, only the altered data is saved. The modifications do not result in a completely new file. Block Deduplication is much more efficient as a result of this behavior. Block Deduplication, on the other hand, requires more processing power and makes use of a much larger index to track the individual bits.

Variable-length Deduplication is an approach that divides a file system into pieces of varying sizes, allowing for better data reduction ratios than fixed-length blocks. The disadvantages are that it generates more metadata and is slower. With Deduplication, hash collisions can be a problem. When a piece of data is assigned a hash number, that number is compared to the index of other Hash numbers that already exist. If the hash number already exists in the index, the data is considered redundant and does not need to be saved again. Otherwise, the index is updated with the new Hash number, and the new data is saved.

The Hash algorithm can produce the same hash number for two separate chunks of data in Rare Instances. When there is a Hash collision, the system will not save new data since the Hash number already exists in the index. A false positive is what this is known as, and it can lead to data loss. To lessen the chance of a Hash collision, some suppliers combine Hash algorithms. Metadata is also being examined by some providers in order to identify data and avoid collisions.

What are the advantages of Data Deduplication?

Backup Capacity

There is far too much redundancy in Backup Data, especially in full backups. Despite the fact that incremental backups only back up modified files, some redundant data blocks are invariably included. That’s when Data Reduction technology like this really shines. A Data Deduplication device can help you locate duplicate files and data segments within or between files, or even within a data block, with storage requirements that are an order of magnitude lower than the quantity of data to be saved.

Continuous Data Validation

There is always a risk associated with logical consistency testing in a primary storage system. The block pointers and bitmaps can be corrupted if a software bug causes erroneous data to be written. If the file system is storing backup data, faults are difficult to identify until the data is recovered, and there may not be enough time to repair errors before the data is recovered.

Higher Data Recovery

The Backup Data Recovery service level is an indicator of a backup solution’s ability to recover data accurately, quickly, and reliably. Complete Backups and Restore are faster than incremental backups because incremental backups frequently scan the entire database for altered blocks of data, and when recovery is required, one full backup and numerous incremental backups must be used, which slows down recovery.

Backup Data Disaster Recovery

For backup data, Data Deduplication has a good capacity optimization capability; doing a full backup every day requires only a small number of disc increments, and it is the data after capacity optimization that is transmitted remotely over WAN or LAN, resulting in significant network bandwidth savings.

Conclusion

As organizations expand their businesses, managing large volumes of data becomes crucial for achieving the desired efficiency. Data Deduplication powers stakeholders and management to handle their data in the best possible way. In case you want to export data from a source of your choice into your desired Database/destination then Hevo Data is the right choice for you! 

Hevo Data, a No-code Data Pipeline provides you with a consistent and reliable solution to manage data transfer between a variety of sources and a wide variety of Desired Destinations, with a few clicks. Hevo Data with its strong integration with 100+ sources (including 40+ free sources) allows you to not only export data from your desired data sources & load it to the destination of your choice, but also transform & enrich your data to make it analysis-ready so that you can focus on your key business needs and perform insightful analysis using BI tools.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite firsthand. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.

Share your experience of learning about Data Deduplication! Let us know in the comments section below!

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