Month: October 2022

Cloud computing is becoming necessary for businesses in the digital era, and more enterprises are investing in cloud data migration. Cloud systems are propelled to the forefront of the digital revolution due to the rising need for integrated solutions that store and aggregate customer data to gain insights and improve the business decision-making process. 

A successful cloud data migration improves scalability, reduces the risk of data breaches or cyber-attacks, and decreases costs, which might derail your business in the long run. However, each cloud migration process is unique, and business professionals should build an end-to-end strategy when transitioning to a cloud environment. This guide will help enterprises learn the importance and challenges of switching to the cloud.

ALSO READ: How Data Analytics Is Transforming The Healthcare Industry?

Importance Of Cloud Data Migration 

A new market research report released by MarketsandMarkets™ indicates that the cloud market will be worth $832.1 billion by 2025. The exponential growth of the cloud is why businesses switch from their on-premise data to cloud-based solutions daily. Some of the main advantages of choosing AWS cloud migration services include the following:

• Improves flexibility and scalability by deploying applications quickly 
• Enhances security as clouds have robust built-in security features 
• Boosts operational efficiency and reduces the overall cost 
• Reduces infrastructure complexities and improves collaboration between teams 
• Improves employee productivity with access to multiple tools, applications, and resources

Challenges In Cloud Data Migration 

Businesses that plan to move to the cloud should identify the struggles and challenges beforehand to ensure long-term cloud migration success. Some of the most common challenges involved in cloud data migration include the following:

• Complexity 

Cloud data migration is a complex process, and things can go awry during or post-migration without expert assistance and strategic planning. Preparation is the key to avoiding the complexities and reducing the overall cost of cloud migration in 2023.

• Cost 

Switching to the cloud involves a few expenses, such as training the in-house team to use the modern technology, charges related to AWS cloud migration services, and upgrading the network connectivity. Business owners must plan their IT budget to ensure that the cost of cloud migration does not exceed the budget limits.

• Security 

When data is transitioned from physical servers to cloud solutions, business owners need to ensure that there are no potential vulnerabilities during the migration. Data should be backed up, compliant with the data security compliance standards, and end-to-end encrypted to avoid the threat of data breach and ensure a safe transition.

• Bandwidth

Many business owners fail to map out the bandwidth requirements and network constraints that become a significant challenge during the AWS cloud migration process. Failure to consider the bandwidth can negate the cloud benefits and result in latency, leading to an inefficient cloud migration process.

• Adaptability 

Adaptability in cloud migration involves how fast the in-house team can cope with the new technology deployed into their business. The team should be ready to adopt a new approach and allocate technical resources, authority, funding, and staffing to make the transition smooth and manage the business operations.

• Vendor Lock-In

Switching cloud vendors can be cost-intensive and time-consuming, and business owners need to research while choosing the potential one to minimize vendor lock-in. 

Inferenz offers AWS cloud data migration services to help organizations transition from on-premise to cloud computing at a reduced cost.

• Interoperability 

All the applications migrated to the cloud are compatible, and experts must focus on identifying the interoperability issues between cloud services and on-premise systems to make the migration successful.

• Downtime 

Network outages will be one of the biggest challenges in 2023 due to the massive volumes of data stored in the database. Interruptions while switching to the cloud and moving large amounts of on-premise data can cause irretrievable data loss, affecting the success of the migration.

• Capability 

Many businesses try to shift their data from on-premises to the cloud without hiring an expert team for cloud data migration. A lack of strategy and capabilities can lead to potential cloud migration issues and overspending due to the repetition of the process.

• Resistance 

Many midsize and large-sized enterprises have to deal with cloud adoption resistance when the in-house team resists the massive change in the organization. Failure to identify and solve the issue early on can lead to delays down the road and increase cloud migration costs.

ALSO READ: Data Structure: How To Drain Data Swamp Effectively?

Avail Cloud Data Migration Services in 2022 

Cloud data migration is the need of the new digital era for businesses of all sizes. The best way to make the AWS cloud migration process successful is by working with an experienced IT partner, as one wrong step can lead to additional risks, such as latency and data breaches.

Inferenz’s cloud data migration services help SMEs and large-sized enterprises migrate to the cloud by simplifying the AWS migration process. Our experts understand the cloud challenges specific to your business, find ideal solutions, and make cloud migration seamless. Contact us to know more!

Data analytics is developing and modulating in new and exciting ways, bringing a digital revolution in multiple industries and transforming the healthcare sector. With the increasing involvement of data analytics in the healthcare industry, data experts can analyze the raw data stored in their database and draw meaningful conclusions that lead to improved patient care and reduced costs. 

Data gathered from disparate sources allow healthcare providers to validate the diagnostic inclinations and track their patients’ ailments, treatment options, and outcomes. Even though there are myriad ways experts can effectively use data analytics, the guide reveals a few ways the latest technology influences the healthcare sector.

Why Is Data Analytics In Healthcare Important?

As per the report of Allied Market Research, the global healthcare analytics market will reach $96.90 billion by 2030 – with a 15.3% CAGR in the past ten years. Data analytics in the healthcare sector provide doctors and administrative staff with the necessary information and data to improve patient care and speed up treatment.

ALSO READ: Best Questions to Ask Before Choosing a Cloud Data Security System

The adoption of new technologies helps medical experts to streamline recurring tasks, ensure the accuracy of the treatment provided, and offer remote care to patients. The data-driven model allows doctors to modify the medical treatment based on the patient’s health history, which secures the future of data analytics in the upcoming years. A few other important aspects of data analytics in the medical industry include the following:

• Healthcare analytics help doctors derive actionable insights and generate reports to improve patient health decisions. 
• Data stored in an organization helps administrative staff schedule doctor appointments and end patient waiting periods. 
• Electronic health records of patients help medical care providers build a database to recommend the best treatments and surgeries to patients. 
• Fitness trackers, glucose monitors, and heart monitors are some of the cutting-edge data analytics tools that will boost patient engagement and help patients become more responsible for their health.

Data Analytics Transforming Healthcare Industry

Business Intelligence and Artificial Intelligence will help healthcare identify loopholes, make strategic decisions, improve operational efficiency, reduce unnecessary costs, and save time by automating recurring tasks. Here are the main aspects in which data analytics will transform the healthcare sector in 2022 and beyond.

• Facilitate Better Diagnostic 

Using data analytics correctly allows healthcare professionals to utilize the stored data to ensure the accuracy and dosage of the prescribed treatments. Patients can benefit from accurate and quick diagnostics when doctors offer care based on previous health data.

Inferenz data analytics experts can help professionals identify the best model, handle the imbalanced dataset, and improve their business with the latest technologies. The team has recently helped a pharmaceutical company from Germany to predict health alerts based on the patient’s vital parameters. Check out the case study here.

• Helping Patients At High-Risk 

Digitized healthcare data allows healthcare professionals to monitor patient behavior and health closely. Tools like predictive analytics will help doctors identify patients at high risk of chronic diseases and deliver personalized services to treat the diseases quickly and accurately.

• Eliminating The Human Error 

A human error in prescribing medications or dosage can not only cause health concerns in patients but also increase hospital expenses. A Business Intelligence tool will focus on the patient’s health history and ensure that the treatment suggested will work positively for the patient.

• Better & Personalized Help 

With the integration of data and analytics in the medical field, healthcare organizations can focus on treating patients based on their lifestyles, genetics, environment, and present health condition. The holistic approach in healthcare will allow patients to leverage the benefits of precise and personalized treatments while helping practitioners to reduce unwanted costs by avoiding ineffective treatments.

• Tracking Patient Health 

Effective preventative healthcare involves identifying a potential health problem and suggesting measures to eradicate it in real-time. Ongoing health tracking will prevent diseases from being severe and help health providers offer the best care at the optimal time. The extensive growth of data analytics in the healthcare sector will assist professionals in tracking patients’ statistics effectively.

ALSO READ: Predict health alert based on patient’s vital parameters

Transform Your Healthcare Organization With Data Analytics

As data analysis continues to evolve and become inseparably tied to different aspects of the healthcare sector, healthcare professionals will follow a holistic treatment approach to provide better diagnostics and improve patient health and wellness in 2022 and beyond. The future of healthcare analytics in the medical sector is fascinating as more professionals will focus on implementing modern medical technology in 2023.

If you are a healthcare expert who wishes to leverage data analytics, contact the data experts of Inferenz. The team can help you identify how data analytics can transform your healthcare organization.

Data is a tremendously valuable asset that helps organizations to get insights into their day-to-day business operations. Using organizational data to make strategic and data-driven decisions enables businesses to grow in the competitive market. However, a lot of structured and unstructured data piled up in the data warehouse leads to a lack of quality control, negating the benefits of data and costing unnecessary expenses. The best way to maintain the quality of the data structure is to drain the data swamp. 

Data swamps, a large opaque pool of data that arrives in multiple formats, are the major obstacle that prevents companies from mining valuable insights and improving their decision-making process. A lack of transparent data governance can cause havoc in data management, making it hard for organizations to extract value from their data. This guide will focus on how to drain the data swamp in 2022 and organize the data lake with meaningful information.

What Is A Data Swamp? 

Using big data strategically could generate $3 trillion in value annually, indicates a McKinsey Global Institute report. When organizations follow a haphazard approach to storing and managing data, it leads to the building of data swamps that slow down data analysis.

ALSO READ: Data Warehousing vs. Data Virtualization – How to Store Data Effectively?

Due to unstructured data in the data swamps, data-driven businesses find it hard to find valuable data and make strategic decisions. The effective data structure in 2022 will help data scientists analyze information without dealing with disparate formats’ tangled mess. Some of the significant drawbacks of a data swamp in an organization include the following:

• The lack of sorted and properly curated data makes it hard for data engineers to access data structures in diverse formats and systems. 
• Continuous data flow in the data swamp exacerbates the problem as data analysts deal with non-functional data that increase complexities. 
• Having irrelevant data affects the overall core function of an organization, leading to less strategic business decisions. 
• Locating and collating data without labeling and meta descriptions is hard, and data analysts search multiple files and sources to find the correct information. 
• Lack of knowledge about data stored makes it challenging to implement clear data governance rules in a data swamp environment, leading to the risk of data breaches.

Difference Between Data Swamp And Data Lake

Data lake and swamp are related to compiling structured and unstructured data in one repository without needing a particular format. A data lake is an organized data structure heap where all the relevant business data is collected, managed, and stored to get essential insights.

In a data lake, data analysts divide the business data and label it to make it searchable and prevent the build-up of duplicate or unusable data. On the flip side, a data swamp is an unorganized space with loads of replication and unwanted data, making it hard for businesses to leverage data to its total value.

An unmanaged data lake can quickly become a data swamp if businesses fail to set guidelines for data relevance. That said, it becomes a cornerstone for organizations to invest in structuring the data lake and prevent it from becoming a swamp.

Inferenz data swamp organize services can help your business avoid unmanaged and duplicate information. Data analyst experts utilize the data lake best practices to structure data and prevent data swamps. Read our case studies here.

ALSO READ: 5 Things To Consider Before Any SQL To Snowflake Migration

Data Structure: How To Drain Data Swamp And Organize Data Lake? 

Following the best practices to structure an organization’s data is the best way to drain the data swamps and access valuable data to make decisions.

• Ensure The Trustworthiness Of the Data 

Companies must ensure that the stored data they use for business insights is reliable, trusted, and readily accessible for data analysts and engineers to make strategic and data-driven decisions. 

• Implement End-To-End Strategy 

Setting standards from the start, like monitoring connections, utilizing the cloud resources, scaling and automating data pipelines, and making intentional data-design decisions, is a dynamic data structuring approach during project planning that helps drain the data swamp.

• Ensure Relevancy Of Data 

Instead of hoarding irrelevant data, organizations must establish specific parameters to remove and clean outdated data. Data obtained from different sources should have a clear purpose of helping the in-house team to make profitable business decisions. 

• Define Data Ownership 

An organization’s vast amount of data can overwhelm the in-house team, leading to data mismanagement. Organizations must figure out who will manage the data pools so that unmanaged data lakes will not become swamps.

Leverage The New Technology To Clean Up Data Swamp

Drowning in the sea of data can affect the business’s profitability and set it behind its competitors. Only a data structure company with cutting-edge technologies can streamline the data management process. They can create the proper data structure and algorithms that help businesses succeed, regardless of how swampy the data lake is.

If you are tired of having irrelevant data in your organization, let the tech-expert team of Inferenz help you to drain the data swamp with services and tech-enabled solutions.

Effective data management is the cornerstone of holistic business development and overall growth in the competitive market. To thrive and yield effective business outcomes, data analysts need to store, manage and utilize data and improve their decision-making process. Conventional relational databases are not efficient enough to handle large data volumes. Snowflake, explicitly designed for the cloud, is a widely used data warehouse solution for SMEs and large enterprises, and that is why most enterprise solutions opt to have Snowflake implementation.

The unique architecture of Snowflake, built on top of AWS (Amazon Web Services), makes it a fast and flexible data management solution. Snowflake gathers all the heterogeneous data from different sources and loads it into a single data warehouse. For efficient data transfer with a standard format, enterprises must plan and execute a Snowflake implementation strategy. This guide will cover the best Snowflake implementation practices for migrating data into the single-stop virtual data warehouse.

ALSO READ: 5 Strong Reasons Why Your Workplace Needs Learning & Development?

Evaluate Virtual Data Warehouse Options

According to SLINTEL, a 6sense company, over 10788 enterprises have shifted to Snowflake data warehousing solutions in 2022, with around 7307 customers from the United States alone. Enterprises should start the Snowflake migration strategy by evaluating the virtual warehouse options in modern database solutions. These are a cluster of computing resources that plays a vital role in executing SQL SELECT statements, performing DML operations, and other essential tasks.

Experts must evaluate different warehouse sizes before picking the one that fits the business’s data needs and workload requirements. It is crucial to determine the warehouse size based on Snowflake’s “auto-resume” and “auto-suspend” features to save costs, as large warehouses require high expenses, leading to the stretching of the budget.

Ascertain The Right Warehousing Size

Another critical consideration for the success of the Snowflake migration strategy is selecting the right warehousing size, and enterprises must focus on aligning the virtual data warehouse size with the workload of the business for successful data migration. Some of the other crucial points to consider during warehouse sizing include the following:

• Recognizing the workload patterns and gradually increasing the size of the warehouse to avoid overloading issues
• Identifying the correct cluster size and improving the individual query performance by scaling the warehousing size up and out 
• Automating suspend/resume by suspending idle time settings and offering end-user workloads 
• Aligning the warehouse size with the workload by keeping the business team in a loop 
• Keeping in mind the credit requirement per hour Snowflake before finalizing the warehouse size 

Inferenz data warehouse and analytics solution framework can help enterprises streamline the Snowflake migration process and transfer data securely. The Inferenz team has recently partnered with a US-based premium healthcare service provider to help them with data warehouse and analytics solutions. Click here to read the case study.

ALSO READ: 5 Things To Consider Before Any SQL To Snowflake Migration

Understanding Credit Charges on Snowflake

Snowflake charges according to the pay-per-second billing that includes the number of clusters, servers per cluster, and time duration for each cluster run. A complete understanding of credit charges will allow enterprises to scale up and down, save costs, and get the maximum profits from the virtual data warehouse. Optimizing resources, computing, and storing charges is another essential factor that will make the Snowflake implementation hassle-free. Here are a few best practices for cost optimization during Snowflake database implementation. 

• Enable proactive notifications and setting on account level if team-by-team quotas are not required to manage resources 
• Compute the viewing usage and resource monitors to cut down costs
• Focus on crucial areas such as dimensional tables, materialized relationships, persistent staging areas, and business rules while storing.

Have A Robust Network Security Policy

For a successful data migration to the Snowflake database, enterprises need to have a tight network security policy for network, object, account, and data.

Some of the best ways to secure data migration include restricting access to specified IP/IP ranges, schema, tables, views, and databases, enabling multi-factor authentication to mandate user access, and following an end-to-end data encryption policy.

ALSO READ: Best Questions to Ask Before Choosing a Cloud Data Security System

Streamline The Data Loading Process 

Breaking the overall migration process into predefined steps can ease data transformation to the Snowflake database warehouse. 

• Focus on the ingestion pattern and transfer the bulk data into tables 
• Prioritize the file size by splitting the larger files into smaller ones and aggregating the smaller files to load faster 
• Prepare a delimited text file with data in ASCII format and UTF-8 as the default character set.
• Enhance the query performance by implementing the clustering key for large tables and using separate warehouses for load tasks and queries

Make Seamless & Successful Snowflake Implementation

Organizations must adhere to the best practices and guidelines to ensure that Snowflake’s implementation strategy is executed well and without errors. Following the above rules will help enterprises propel toward a successful implementation that allows them to achieve expected goals and outcomes. 

Inferenz can help you switch to Snowflake, the fully managed cloud data warehouse, to store data, run analytics, and make better business decisions. Feel free to contact us to learn more about our Snowflake implementation strategy.

As data is a valuable asset for an organization to make insightful decisions, data analysts must capture, manage, structure, and analyze the data appropriately to achieve a positive return on investment. Enterprises need to handle the large influx of data accumulating in their database daily and categorize it into structured data. They must follow the best practices for data structure effectively and access vital information lurking in all the stored data. 

Modernizing data architecture is one of the best ways to handle massive data volumes and leverage them to make decisions that bring profits. Effective data management practices can break down the barriers of dealing with duplicate and unwanted data stored in disparate repositories. This guide will reveal the best practices for data structure in 2022 that organizations can follow to get a competitive edge and generate better revenue.

What Is A Data Structure?

Allied Market Research indicates that the business analytics market and big data will grow by 13.5% from 2021 to 2030, reaching an estimated $684.12 billion. The data structure is a simple way to organize data in an organization. It makes it easy for the in-house team to collect, organize, store and retrieve data quickly and effectively. Data structure and algorithms go hand-in-hand and are essential for turning organized data into useful information. Some of the most critical reasons why data structures are crucial include the following:

• High processing speed – Data analysts organize data into easy-to-use formats, contributing to faster processing of large amounts of data. 
• Make data searchable – When businesses have information stored in a data structure, the team can conduct searches and get the desired information quickly. 
• Reusable – Implementing a data structure and modernizing the data architecture allows data analysts to reuse it without needing a new system. 
• Easy-to-handle data requests – Different teams are searching for data to make organizational decisions and strategies. Data structures arrange the information to help users instantly access the required data.

Best Practices For Data Structure In An Organization 

SMEs and large enterprises have large amounts of data available from disparate sources. Proper utilization of different data structures can help businesses generate significant insights that boost data-driven decisions. Eliminating unstructured data, organizing the relevant material, and illustrating the data processing in a data architecture diagram can help businesses stay updated and maintained. Some of the best practices for enterprise data architecture and structure include:

• Clean The Unstructured Data

Ronald Van Loon, CEO of Intelligent World and a leader in data and analytics, in his webinar entitled “Your Future in Data Science: Career Outlook 2020,” focuses on statistics that 65% of the businesses couldn’t analyze or categorize all the data they stored, indicating the need for data structuring.

ALSO READ: 7 Things Modern L&D Professionals Should Be Doing

Adhering to the stringent data cleaning rule can avoid damage to the entire data collection while helping teams to stay away from random sources. Data analysts should acquire data only from dependable sources to construct an organized database for practical analysis and real-time access to business data.

• Eliminate Internal Data Silos

Part of the data structure heap is modernizing the data architecture that makes internal data accessible to all the team members. For many companies, internal data silos are a significant barrier to data structuring as it leads to poor data management practices and inefficiency in business. Data architecture modernization cleans and validates the data to determine which information is complete and accurate to gain insights.

• Leverage The Advanced Technology 

With the advent of new data structuring technologies, it has become easy for organizations to retrieve and store data for appropriate usage. Companies can determine the project’s needs and compare it with the potential technological stack to make the final choice of the right tool.

Inferenz data structure services involve helping businesses leverage the ever-evolving technology to eliminate the manual process of structuring data. The tech-expert team of Inferenz will help you mine data by implementing the latest technologies and ensure your in-house team gets data from trustworthy sources. To learn more about Inferenz services, read the detailed case study here.

• Focus On Scalability 

When SMEs grow into large enterprises, it leads to the accumulation of data that requires scalable solutions. Extrapolating the future of data volume will allow organizations to select reliable solutions and enable real-time reporting and time analysis. That said, the data analyst team should focus on choosing agile, scalable, and flexible solutions to meet the growing demands of the enterprise.

ALSO READ: Artificial Intelligence System Development in 2022

Implement The Best Practices For Data Structure With Experts 

Embarking on data structuring and data architecture modernization initiative involves expertise, knowledge, and access to the right tools. Businesses must pay attention to enterprise-grade data quality technologies that transform unstructured and raw data into dependable insights. 

If you want to implement the best practices for data structure without degrading data integrity and quality, you can reach out to the expert team of Inferenz. The tech experts of Inferenz can help you put the best practices in place and ensure your team gets access to valid, clean, and reliable information.

Enterprises have been reluctant to move their business data from on-premises to the cloud due to data security issues and data breaches. However, with the advancement in the tech industry, organizations are now leveraging cloud migration or a cloud data security system to improve business capabilities, enhance operational efficiency, and improve customer experience without worrying about data loss during migration. 

As the cloud industry is growing explosively, many cloud service providers are continually innovating, evolving, and delivering high-quality services to stand ahead of the competition. A managed service provider can implement an end-to-end cloud migration strategy to ensure successful data migration and mitigate data security system problems. This guide will focus on the questions every business owner should ask before choosing a cloud data security system. 

How do they ensure the security of cloud data while migrating?

Cloud adoption has been expanding for several years as new trends take shape, enabling business owners to leverage the benefits of the cloud. A study by LogicMonitor indicates that nearly 30% of the on-premise workload will shift to the cloud by 2025. 

Even though transitioning from physical servers to the cloud can improve business operations and secure data, business owners must focus on strengthening the security system to eliminate potential vulnerabilities before data migration. Here is what an experienced team will take into account to improve network security. 

• Data backup and end-to-end encryption 
• Monitor the data systems to identify threats in real-time
• Effective measures to mitigate the short-term vulnerabilities 
• Creates a backup IT environment to run applications until the cloud migration is complete 
• Compliant with data security compliance standards

What security systems does the cloud migration service provider have?

Many tech leaders are planning to move their data to the cloud; however, the security of cloud data systems remains a concern. Only a reliable and well-managed cloud data migration company can protect sensitive data from a breach with a high-level security system that includes the following: 

• Identity management and secure user endpoints to reduce the risk in cloud dataflow 
• Authentication security to control application and data changes 
• Access control to protect sensitive business information 
• Secure connections between internal data and the cloud with standardized APIs
• Sophisticated threat intelligence, extensive automation, and extended visibility 

Do the cloud service providers have a well-qualified team of data security professionals?

Moving the entire data to the cloud requires expertise, knowledge, and skills to ensure no data loss or breach midway and smooth data migration. A reputable cloud service provider leverages state-of-the-art security controls and round-the-clock guards to boost technical security and continuously monitor attacks. 

Data security professionals create and maintain a secure environment with due diligence to protect server data in the cloud. Inferenz data security professionals can help you remain vigilant and make the data transfer to the cloud a breeze by using its cutting-edge technologies. Read our detailed case study to know how we leverage tools to help our clients here. 

What cloud security certifications does the cloud migration service provider have?

Lee James, who is the hosting provider Rackspace CTO, believes that multi-cloud environment systems are the future of cloud computing, Server space indicates. Business owners who want to switch to multi-cloud must look for a certified migration service provider to deal with data security and migration problems.

Security certifications prove that the provider legally excels in the security standards and meets the current and future cloud security requirements. 

What shared responsibility model does the cloud migration service provider follow?

The business owner and the cloud service provider enter into a partnership of shared responsibility for security reasons. A cloud security and risk framework outlines which data states, security duties, and locations are in the business owners’ domain and which are in cloud service providers. The three main types of models include Software as a Service, Platform as a Service, and Infrastructure as a Service.

ALSO READ: 5 Things To Consider Before Any SQL To Snowflake Migration

Before entering into the partnership with the provider, reviewing and understanding the shared responsibility can make moving data to the cloud manageable and secure. Some of the other benefits of choosing a shared responsibility model for a data security system include the following:

• Reduces customer responsibility for the cloud dataflow 
• Less management of infrastructure and reduced extra costs that stretch the budget 
• Relieves operational burden for the in-house team as they can focus on other tasks 
• Improves cybersecurity by reducing the risk of vulnerabilities and data breaches 
• Delineating which responsibilities the cloud service providers take on to enhance the productivity of the business team 

Make Digital Transformation Easy With Experts Of Inferenz

Data security in 2022 has become the main apprehension for organizations of all sizes. Performing timely backup, restoring data, and choosing the right cloud service provider can protect a business’s sensitive data and critical information. 

If you want to secure the data during cloud migration, Inferenz data security professionals can help you with a well-structured migration strategy to move data smoothly from the database to the cloud data security system.