Top 10 Data Warehouse Trends Now & For Future

IT executives are working with more data than ever before. And the volume is growing by the day.  It's not a simple challenge to tackle, but there are several breakthroughs on the horizon that will shake up the business in the coming months. 

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About The Author
Nitin Nijhawan, CDOicon

Table of Contents 

1. Introduction 

2. 10 Data Warehousing Trends To Watch Out 

3. Considerations for choosing the latest data warehouse trend 

4. How VLink’s data warehouse experts can help you? 

5. Key takeaways 


The data warehouse, the lynchpin of every contemporary data stack, lies at the heart of it all.  It is always changing. Emerging technologies such as virtual data warehouses and AI-powered data analysis tools are transforming how government agencies use data. 

According to the reports of IDC, over two-thirds of data practitioners feel they are expected to make data-driven decisions, while just 30% believe that their actions are truly backed by data analysis. They discovered that just over a quarter (27%) of data practitioners completely trust the data they deal with in its most recent Data Trust Survey. 

In recent years, firms of all sizes have used this technology to develop a data-driven strategy. Cloud computing has accelerated the use of contemporary data warehousing techniques. 

In this blog, we’ll talk about the trending data warehouse solutions to watch out for the future as well as in the current era. 

1- AI & Machine Learning adoption for smart operations 

AI and machine learning (AI/ML) are finding practical applications across many sectors. AI can help with everything from insurance claims management to supply chain optimization and fraud detection. 

  • Discovers relationships. 
  • Evaluates potential outcomes. 
  • Routine choices are automated. 

Despite the gains such technologies provide, data practitioners understand large-scale automation may exacerbate the problem of poor data integrity. As the volume of data being handled grows, organizations will increasingly need to shift data operations to quicker machine learning-enabled AI systems.


Increasing volume of data year by year


AI/ML algorithms can only provide credible insights if they are trained on large amounts of accurate, consistent, and contextualized data. Systems educated on faulty data may generate erroneous suggestions or wrong conclusions, which might have severe consequences.  


2- Zero-trust architecture for enhanced data security 


Nearly 85% of the business leaders in the study said they believe cyber risks are growing and only 35% say their companies are properly protected. 

When IT staff and decision makers were asked about the most important aspects of data management and storage for the next three years, 61% rated data protection and availability as one of their most important factors. Here are the various data types that are recognized as most critical for breaches and thefts: 


Categorization of types of data and their significance


This approach is different from traditional cybersecurity models in which users can go anywhere in the network once they’re granted initial access. With zero-trust architecture, every interaction between a network and a user is authenticated, authorized, and validated. 

Because ransomware attacks are increasingly focused on backups of data, creating immutable backups is another strategy companies are using to avoid a data crisis. 

Zero-trust architecture works through the write-once-read-many (WORM) mechanism—as employees upload files, they set an immutability flag that locks the files in place for a set period. the data is frozen for that specified duration. 


3- Deploying DataOps for continuous improvement 


DataOps is a collection of technologies, processes, and best practices that combine a process-focused approach to data with the automation methods of the Agile software development methodology to improve speed and quality while fostering a collaborative culture of rapid, continuous improvement in the data analytics field. 

DataOps strategies share these common elements: 

  • Collaborative interactions between businesses & data scientists 
  • A flexible and customized data development environment. 
  • Fast optimizations and automated processes. 
  • Ensuring quality of data through automated testing. 
  • Accurate monitoring and reporting of analytical processes. 

DataOps employs lean and agile principles like continuous development and testing to optimize data flow communication, integration, and automation, resulting in more predictable data delivery. 



Furthermore, it provides IT experts, data engineers, data scientists, and data analysts with information about the outcomes of their testing, allowing them to swiftly iterate viable product solutions. 

Consequently, organizations can get their data analytics apps up and operate considerably faster than before, giving them a competitive advantage. 

4- In-memory computation for faster outcomes 

Clusters of servers are used in in-memory computing to pool total available RAM and CPU power. This architecture spreads data handling and processing responsibilities throughout the cluster, resulting in dramatically increased performance and scalability. 

Although it was originally popularized in the financial services industry, in-memory computing has seen tremendous development amid the recent transition to remote work and has also emerged as a trend in data warehousing. 

The importance of IMC will continue to increase over the coming years as ongoing development and new technologies become available including: 

The significance of in-memory computation is increasing every year, and following advances are available to deploy this data warehouse trend: 

  • Support for distributed SQL – which includes ANSI SQL-99 support, minimizing the occurrence of manual errors through inappropriate queries. 
  • Non-volatile memory (NVM) – helps in preventing fault-tolerance through software applications, enables predominant computing storage. 
  • Hybrid Models for storing large datasets – supports a universal interface for platforms, RAM, in-memory spaces, and disks to adjust processing performance and storage strategies. 
  • Record-maintaining platforms – to support hybrid transaction & analytical processing (HTAP) with persistent storage layers for flexible and quick information access. 

Because of the continuous work-from-home practices that many organizations have embraced, this data warehousing trend looks to have established a permanent practice across all industries. 

5- Real-time data streaming 

Standards for data freshness and latency have become closer to real time as data demands have changed. We will witness the growth of solutions that simplify the difficulty of programming code to construct real-time streaming pipelines in areas such as banking, ecommerce, and manufacturing. 

Data warehouse solutions like Snowflake enable organizations to prep, merge, enhance, and query streaming data sets in SQL, which they say has a 12x better price-performance than a typical data warehouse. Snowflake has also refactored its Kafka connector such that as data arrives, it has faster query processing, resulting in a 10x reduction in latency. 

6- Energy-efficient data mitigation & storage 

While the amount of data is steadily increasing, the energy required to store all of the data is as the amount of data increases, so does the amount of energy required to store it all. These massive air conditioning requirements use more than 40% of a data center's power. 

Data centers create a large amount of heat, and extensive cooling is necessary for all the gear to work properly. These massive air conditioning requirements use more than 40% of a data center's power. 

And that's where Green Data Storage is born. 

Cloud data warehousing is increasingly being included in energy-saving programs for enterprises. It's encouraging to see a green data warehousing trend emerge as we work to combat climate change. 

Switching to a green data warehousing solution, such as a cloud data warehouse, may provide other advantages in addition to energy savings. It is good for the environment and will lower your organization's carbon impact. 

7- In-database analytics 

In-database analytics is a type of data analysis that takes place within the confines of a database or data warehouse. After transfers, in-database analytics are embedded into the storage architecture and replace the usage of external programs. 


How in database analytics work


By doing operations on the database rather than the BI tool, you eliminate the resource-intensive pull phase, and you now have portable code within the database that you can transfer to another tool. In terms of security, just the modeling, query, and results are communicated between the database and your BI tool.  

Performing data analytics procedures on the inside decreases data movement, bandwidth overhead needs, and security concerns associated with disseminating sensitive data across various sites and devices. 

8- Integration Platform-as-a-Service (IPaaS) 

Integration Platform-as-a-Service (IPaaS) is used by large companies to combine data and applications that reside on-premises as well as in public and private clouds. This enables the development and deployment of complicated integration projects involving two or more connections to SaaS as a technology connector for common databases. 

Traditional ETL is a method of combining data from many systems into a single database, data store, or data warehouse for analysis and decision making. iPaaS systems communicate data through API endpoints and provide security through API rules such as data and authorization. 

9- Smart compressing of data 

As organizations stockpile more data, they have recognized the necessity to compress their data and conserve storage space. Companies are increasingly relying on compression techniques to minimize rising storage costs as their stored data grows. 

Data compression is the process of reducing the number of bits (binary digits) required to hold data. It works by first establishing reference libraries for binary data's 1s and 0s, then replacing lengthier strings with shorter reference tags. 

Data compression frees up storage capacity, speeds up data transfers, and lowers overall storage expenses. That is why it has been one of the most popular data warehousing trends for quite some time, and it is likely to continue. 

10- Hadoop integrations 

With its distributed file system (Hadoop DFS) and parallel MapReduce paradigm, the open-source Hadoop application excels at processing very massive data volumes. 

As a result, Hadoop is an excellent complement to "standard" data warehouses, which explains why an increasing number of data warehouse managers are turning to Hadoop to handle some of the most demanding workloads. 

The application's popularity has risen and fallen over the previous decade, but it has recently seen a resurgence in recent data warehousing trends. 


Considerations for choosing the latest data warehouse trend 


Large businesses and IT executives are striving to establish and own a cloud strategy for data storage in order to shift some or all an organization's data warehouse architecture to the cloud.  

Here are some technological considerations that will help you choose the best data warehouse trend: 

  • The biggest public cloud providers provide a wide range of data-related services and are constantly innovating. There are also other third-party choices to consider. 
  • Your data-related technology choices, as well as the plan for implementing them, must be consistent with the business strategy and goals. It is also necessary to examine the capacity to support or transfer your existing data moves, ETLs, and security model. 
  • Using a partner with cloud platform and data transfer knowledge is frequently something to think about. This is particularly true when it comes to technology selection and relocation planning. 

Databases cannot match company demands for flexibility and computational power, and a standard data warehouse is inefficient for most firms. Cloud data warehousing has emerged as the greatest choice for businesses seeking to make informed business decisions while remaining competitive.  


How can VLink’s data warehouse experts help you? 


With the proliferation of data sources and kinds, the development of hyper-scale data warehouses with strong computational power and large storage capacity will become crucial. The integration of cloud computing technologies with data warehousing, as well as the availability of low-cost big data solutions, will continue to change how businesses use data analytics. 

VLink offers multiple benefit from end-to-end data engineering and analytics across verticals, while helping you pick the proper methodology, and expedite data intelligence transformation by engaging with latest data warehousing trends in the market. 


Get rid of the data related problems by deploying the most effective and advanced data warehouse solutions for VLink

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