The complete guidebook to Enterprise Data Management

Today, data drives the business world. To gain a competitive edge, the enterprises ought to manage the data that transmits through their processes and systems. This article aims to take you through enterprise data management to transform your business digitally.

Enterprise data management (EDM) helps business enterprises to determine, integrate, and access the data for internal and external applications. It is a set of processes that focuses on data quality, accessibility, accuracy, security, and governance. It closes the gap that arises due to the mishandling of the data.

Why business needs enterprise data management?

EDM creates a uniform system to store, manage, monitor, access, and share the data. This standardization process helps businesses in monetizing the data available.

1.    Real-time data

Real-time Data is valuable. If data is left unchecked, the authenticity decreases. Such kind of data is not resourceful to the company.

2.    Competitive Advantage

Centralized data is valuable. EDM helps you gain governance. It also ensures that end-user gets access to a single, authorized source hosting quality data.

3.    Reduced Costs

EDM frees up the staff that was supposed to cope with low-quality data. Now, they can be utilized somewhere else. An experienced enterprise data management service provider can help you reduce these costs.

4.    Improved customer experience

Even the tiniest mistake can ruin your reputation in front of the customer. With centralized data management processes, you can serve your customer better.

5.    Compliant data & Streamlined operation

With the increase in data, chances of compliance risk increase. Proper data management enables you to discover hidden data assets and evaluate their values & risks. Streamlined operations help you understand the processes better. With instant access to operational data, an informed decision can be made specially to meet security and compliance obligations.

Benefits of Enterprise Data Management

A well-defined enterprise data management helps you to set up rules & regulations that unify data across the workflows. The uniformity in the data brings the following benefits with it:

• It improves operational efficiencies that in turn reduce costs
• Facilitates data-driven culture.
• Helps to pace up launch of new products
• Helps in discovering new opportunities and services that can increase revenue sources.
• Gives space for innovations.
• Increase responsiveness to consumer needs & market changes.
• Alleviate the risk of losing business-critical data.
• Reduces duplication of data that helps in saving time & resources.

Implementing an enterprise data management system is not just a trend. It is an operational need that demands addressed. Enterprises must enhance their capabilities to use their data to boost efficiency, derive informed decisions, and pump up the organization’s growth.

Key Elements of Enterprise Data Management

EDM consists of the following six smaller subsystems that elucidate creating a future-ready EDM strategy –

1.    Data Governance

It is the foundation layer of data management where all policies, rules & procedures for combining data from different sources are made. It streamlines the information flow, safeguards user’s privacy, and check compliance

2.    Data Integration

It creates a centralized repository of data that is easily accessible to all stakeholders. It grants the appropriate responsibilities to the right people.

3.    Master Data Management

It is a combination of tools and processes that manages, stores, organizes & syncs master data of the business. It offers a collaborative view of data lying in the silos.

4.    Data Architecture

It creates instruction that streamlines data extraction, integration, transformation, and data modeling. The goal is to set up an architecture that organizes and secures operational infrastructure.

5.    Data Warehouse & Business Intelligence

Data warehouse stores data coming from different sources. It uses a suitable format to derive insights easily. Business intelligence enables the user to make data-driven decisions for better business outcomes.

6.    Metadata Management

It creates a standard procedure that governs the management of data definitions and related metadata.

Challenges faced in Enterprise Data Management

In the years to come, organizations that fail to look at their data management policies will struggle to manage and organize the data. The following problems can occur when the data storage and management is unorganized –

• Operational inefficiencies and inaccuracies
• Conflicting data
• Inconsistencies and error in reporting
• Time and effort wasted to streamline data for processing
• Dissatisfied customers

Conclusion

Companies feel that EDM is a daunting process. However, with a proper strategy and technology partner, you can develop and implement efficient EDM solutions.

Share your thoughts on how you are implementing enterprise data management in your business at contact.us@virtuetechinc.com.

Data Engineering Ecosystem

Companies have started realizing the value of data. What is often ignored is the amount of engineering required to make this data accessible.

Data engineering solutions have three core functions.

1. Data-Intensive API & Application
2. AI & ML projects
3. Data warehousing-based business intelligence and reporting

For every use case, the data pipeline varies. It is a difficult task to choose from the pool of tools and technologies. A data engineering ecosystem comprises of these tools. Data engineering services use generally defined frameworks for visualizing data pipelines and the various data engineering tools. Each tool of the data engineering ecosystem belongs to a different category. Let us look at the components of these ecosystems.

Data Ingestion

The foremost task is to get data into the system. Three main strategies for data ingestion are:

1.    Batch ingestion

The first step of the data engineering ecosystem is uploading data files in batch style. It can be done using a basic function of programming languages or using common data-transformation libraries like Spark and Pandas

2.    Stream Ingest

It includes high throughput messaging system with computation capabilities. Widely used open-source tools are Kafka & Flink. Other available tools are AWS streaming and Google Pub/Sub.

3.    Managed SaaS Ingest

Data ingestion can be done from operational systems like Salesforce CRM, Hubspot account, and internal databases. Rather than fetching data from these tools, use pre-built data connectors for ingestion. Tools available in this category are Segment, Stitch, Fivetran, Snowplow, and Matillion.

Data Storage

            Data is stored using data lakes in data science engineering. The data lakes are centralized repositories that store structured and unstructured data. The two main architectures of data storage are:

1.    Object storage

The object must efficiently store the data so that the data can be easily consumed by applications present in the data engineering ecosystem. All cloud-based object storage applications such as AWS S3, ADLS, GCS are examples. Other examples are wasabi, Pure, & MinIO.

2.    Analytics Engine

These engines provide a SQL interface to the tabular and relational database. Analytics engine like Databricks Lakehouse, Dremio, and BigQuery offers computations only. Some analytical engines of data science engineering like Snowflake, Druid, and Redshift offer storage services with analytical engines.

Metadata Management

                        Metadata is used to define schema, data types, relations to other databases in data engineering services. It helps in improving manageability and in adhering to good practices. Some metadata management formats are:

A.    Open table formats

They improve data mutability and maximize performance by giving freedom to store data in user desired format. They achieve it by managing metadata files over the dataset, allowing fast read and write operations. Hudi, Iceberg, and Delta are some tools that give open data formats provision in data engineering. 

B.    Meta stores

They abstract files in object storage and transform them into a query-able table. Hadoop’s Hive is the sole meta store available. It provides tabular access to the content.

C.    Orchestration

Data pipelines require streamlining of tasks as it includes thousands of jobs where the input of several jobs is dependent on output. Open-source applications like Airflow, Dagster, and Perfect offers orchestration in data engineering services.

Computation

            Data is present in the data engineering system after data ingestion. It is time to crunch it.

To handle data volumes, distributed compute load is a must. This category has evolved to give near real-time computation through SQL and code interfaces.

1.    Distributed computing

It is dominated by Spark, an open-source technology available almost on every cloud provider.

2.    Virtualization

It gives access to data via a single endpoint regardless of the location of data. PrestoSQL was the first one to provide such services. Now, every cloud offers its version of PrestoSQL.

Data Science & Analytics

            Certain tools in the data engineering ecosystem are developed to improve business intelligence and data science functions. These data science tools are divided into the following categories:

1.    MLOps

Interest in the development and maintenance of machine learning models has increased exponentially in the last few years. A dozen of open source tools present are MetaFlow, DisDat, and KubeFlow(Google).

2.    Analytics workflows

Analysts face a challenge in the organization and execution of transformational queries. DBT and Dataform have come to the rescue for running data-intensive code/SQL.

3.    Notebooks

Notebooks have become preferred tools for exploratory analysis, ML model training, and production ETL jobs. Jupyter, Deepnote, and Hex are players in this segment.

Organizational Metadata 

The tools in this section aim to enhance the usability of data platforms in an organizational context.

1.    Discovery

The discovery tools help users find datasets easily, visualize connections among them, and see how they are used.  At an organization level, it is important to make information readily available to promote a data-driven culture that is efficient and consistent.

2.    Manageability and Governance

Organizations are looking for data auditing, reproducibility, and governance in data engineering services. Tools in this segment simplify data management and governance.

3.    Quality & Observability

This segment offers rules or ML-based data quality monitoring and testing. Errors and anomalies are likely to occur in data engineering solutions. These tools aim to identify those errors before your consumer finds them out.

Conclusion

            We have looked at different segments present in the data engineering ecosystem. Though it might seem hard to implement, it is a future safe and wise decision to use these while finding data engineering solutions.

Please share your thoughts on data engineering at contact.us@virtuetechinc.com, and we can also discuss your business requirements if any.

Data Engineering as a Service

Today, every business centers around data. It makes the study of data mandatory. As soon as you hear the phrase ‘study of data’, words like data science, data engineering pops into your mind.  Often, people use data science and data engineering interchangeably that is incorrect. So, let us try to draw a line between data science and data engineering service first.

Analysis of data is a quite challenging task as data is managed by different technologies in different structures. Data science tools assume that data coming from various sources are stored in the same format. But the reality is far away from this assumption. Here, data engineering solutions come into the picture. Data engineering services are designed to make data more accessible and interpretable for data consumers. They transform data into a uniform structure so that it can be easily comprehended by data scientists and analysts.

Data science is about consuming data curated by data engineers. They further analyze these data and communicate their insights using data visualization tools.  

Responsibilities of Data Engineering Solutions

Crucial responsibilities of data engineering as a service are:

• Data requirement gathering: It answers the questions of how long data needs to be stored, what is its usage, who should access this data.
• Maintaining metadata: It is responsible for maintaining information about data such as the schema, the data size, the source of data, and the owner of the data.
• Ensure Security and governance: It talks about what central security protocols are used, how data is encrypted, and how to audit the data.
• Data Storage: It finds out the most optimum platform to store the data.
• Data processing: It transforms and enriches the data, summarizes it, and stores it in the storage system. 

For upholding these responsibilities of data engineering as a service, data engineers perform the following tasks:

1. Acquire data from different data sources
2. Cleanse the data and remove the errors.
3. Convert data from one format to another.
4. Interprets data that has multiple meanings.
5. Removes duplicates copies of the data

How can your organization benefit from Data Engineering as a Service?

• It will help in simplifying your data architecture that will reduce costs and increase profits.
• It will assist in speeding up the process of accessing insights.
• It will act as an unsung hero for improving your product quality and user experience.
• It helps in making better decisions based on the knowledge you gain from the data.
• Better aggregation of data will help you in identifying new business opportunities.

Applications of Data Engineering Solutions

1.    Automating Data Processes:

Data engineering services will enable you to convert your old processes into the automated pipeline. These automated pipelines can manage from simple file transfer to complex data processing and modeling using various tools and technologies.

2.    Serverless Data Processes:

Data engineering solutions will promote migration to the cloud by creating serverless data processes using cloud-based products. One can select the cloud services and platforms based on the requirements.

3.    Data Processes in Dockers:

Data engineering as a service will help you in developing your data processes into dockers. It makes the deployment of data processes to the client’s production environment significantly easy. It helps in duplicating and deploying these processes to multiple systems readily.

4.    NLP and Data Analytics:

Data engineering services create a master data set and help in building data pipeline-driven ML engines. Automated document tagging for knowledge management documents will aid in natural language processing.

Conclusion

            Everyone is focusing on data science to draw insights and leverage the benefit from the data. What people forget is to optimize and improve the data science processes. Data engineering services let you simplify existing data science solutions and adds value to the business by saving costs and time.

Reach out to us at contact.us@virtuetechinc.com to find out the scope of data engineering in your business.

Data pipeline strategies every data engineer must know

The idea of an ideal data pipeline is nice but it is different from the real world. The variance between theory and execution is huge. People generally make suggestions without considering the reality. It is one of the main reasons for the data pipeline mess. Let us start with understanding what a data pipeline is.

What exactly is a data pipeline?

A data processing pipeline is series of connected processed that help in moving data from one point to another. In such an automated data pipeline, data can be transformed during transmission. The mode of transformation can be sequentially linear or parallel execution.

The word pipeline is enough to give us an idea of the difficulty in building and maintaining. Consider these processing pipelines analogous to the real-world pipeline. It may take a lot of time to find out the actual cause of the disruption. The challenge will be to build an opaque data analysis pipeline that makes problem detection easy.

A database pipeline should be built to maintain data quality. Other vital aspects are fitness, lineage, governance, and stability. It must have a contingency mechanism if in case the pipeline breaks down. Next, we look into ten strategies for building data pipelines.

10 Strategies for building and managing a data pipeline –

1.    Understand the existing solutions

Before building anything, understand what solutions are existing. Know their data models, their loopholes. Try to anticipate vulnerable aspects of the systems from which data is importing and to which the data is exporting. Take a note of all such observations. Also, make a question list that needs to be answered beforehand.

Implementing a new solution is a good idea. It will be one of the best solutions when it integrates well with existing legacy architecture. Compatibility analysis of the tools may end up saving a lot of time and effort.   

2.   Build block by block

Build your big data analysis pipeline incrementally. The main reason is to save you from building something which doesn’t serve the purpose. Sometimes, requirements are also unclear until your customer asks for something at the last moment. Imagine the rework you have to do if it’s unsupportable.

3.   Document your goals

Goals tend to evolve you start building the solution. It is advisable to create a live document mentioning all the targets. Keep revisiting and updating whenever required. Also, ask others to document their goals as well. We tend to presume that others are thinking the same as we are. So, listing down the objectives will help in bringing your team on the same page.

4.   Build to optimize costs

The actual cost will be higher than the estimated costs. If something grows exponentially, remove it from the paid platform and include it in the data ingestion pipeline. In this way, it will minimize the cost of operation.

Another good tip will be to overestimate your costs by 20% while planning the budget.

5.   Identify stake and tolerance

Stakes are generally high for a low tolerance system. It requires careful planning. The benefit of dealing with data is decisions taken are easily reversible. It means at any point decision can be changed. Understanding what is at stake and how tolerant is system helps us in understanding and guessing the breaking point of the system

6.   Create functional working groups

Rather than working in the waterfall model of completing a task, working in agile like methodology will help you manifold efficiency. Create a small team that helps in achieving one functional unit of the machine learning data pipeline. The team will comprise team members of various expertise areas.

This approach gives data engineers room to bring the feasibility of a solution to the discussion table.

7.   Implementing observability tools in the data pipeline

Observability tools help you to understand what’s happening in the data pipeline. Observability tools encompass: monitoring, alerting, tracking, making comparisons, analyzing, recommending the next best actions. They diagnose the root cause of your processing pipelines issues.

8.   Create a decision tree to decide the usage of a new tool

Use a decision tree to decide whether you should add another tool or adjust the existing one on your data science pipeline. One should refrain from adding a new tool until it is necessary as the addition of a new tool will incur additional costs and efforts in maintaining.

9.   Check your solution from four dimensions of data quality

 Check if your data analysis pipeline stands solid on aspects of fitness, lineage, governance, and stability. A good solution will be well balanced on these aspects.

10. Document the work done

Make a habit of documenting the work done with evolution made to the system. Creating a log file helps you in detecting the probable cause of issues. It will add durability to your visualization pipeline.

Conclusion

            A well-planned data pipeline will save efforts, time, and money. So, pick out the best-suited solution and evaluate it from 4 aspects of data quality. Comment your thoughts on the strategies mentioned above.

Beginner’s guide to AWS data archiving

With high-speed internet, on-cloud applications, data creation, and consumption rates have increased significantly. According to Statista, Internet traffic will produce approximately 79 exabytes per month.

Factors responsible for increased data creation and usage are

 1. Migration of on-premise applications to the cloud.

 2. Increased interest in AI, ML.

 3. Wide application of data analytics.

Customers wish to seek solutions with enhanced data durability, fast response time, more security, and greater data accessibility. For organizations, data is of the utmost importance. The data is crucial for advanced analytics and business intelligence. Regulatory compliance also requires longer retention of data.

To cater to the need for longer retention of bulk data, AWS offers a comprehensive set of cloud storage services. Let us walk through them.

Amazon storage classes: AWS S3 GLACIER and AWS S3 GLACIER DEEP ARCHIVE

Amazon S3 Glacier and S3 Glacier Deep Archive are S3 storage classes for data archiving and long-term backups. They are secure, durable, and economical.  These solutions aim to provide 99.99% durability, comprehensive security, and compliance with stringent regulatory norms. It is highly feasible compared to on-premise solutions. It costs as little as $1 per terabyte per month. To meet varying storing and retrieval needs, AWS provides the following options.

AWS S3 GLACIER provides you the facility to achieve data from a few minutes to several hours. S3 Glacier Deep Archive gives you two options ranging from 12 to 48 hours.

Benefits of using AWS archiving services

1. Quick retrieval: The amazon S3 glacier storage provides three retrieval options to meet your demands. For hot data use cases, retrieval typically takes 1-5 minutes. Standard retrieval takes 3-5 hours that is ideal for less time-sensitive needs like data backup, log-term analytics, and media editing. Bulk retrieval is the lowest cost storage option that returns a large amount of data in 5-12 hours. The Amazon S3 glacier deep archive gives two retrieval options ranging from 12 to 48 hours.
2. Unparalleled scalability and durability: These AWS services run on the largest global cloud infrastructure. Data is distributed among a minimum of 3 physical locations that are separated within an AWS region. It helps in achieving high durability and scalability.
3. Unmatched security and compliance capability: These AWS storage classes can be integrated with AWS Cloudtrail to log, monitor, and retain API call activities. It helps in auditing and encrypting. These classes support various standard and compliance certificates too. Amazon S3 Object Lock integrates with the storage classes to satisfy compliance requirements for almost every regulatory agency in the world.
4. Economically feasible: They are the lowest cost Amazon S3 storage classes that allow archiving a large amount of data at the least cost. It follows a pay-per-use model with no minimum commitments. This characteristic makes it feasible for analytics, IoT, A, ML, compliance, and media asset archiving.
5. Easily integrate with many partners: AWS includes thousands of consulting, system integrators, and software vendor partners. AWS Partner Network partners have adapted their services and software to work with AWS storage classes. It provides solutions for backup and recovery, archiving, and disaster recovery.

Use Cases

• Archiving Healthcare Information – Hospitals need to retain the medical records of the patients for decades to meet the regulatory norm. Amazon storage classes help you in storing petabytes of old data at a relatively low cost.
• Regulatory & Compliance Archiving – Amazon S3 object lock helps in setting compliance controls. It is useful in many financial and healthcare services.
• Storing Scientific Data – For any research, organizations generate, analyze, and archive a huge amount of data. Amazon S3 Glacier and S3 Glacier Deep Archive storage classes retain the data without bothering about the complexities of hardware and facility management. ‘
• Storing Media Assets – Media assets such as news and video require high storage volumes and can grow to petabytes over time. The amazon storage classes allow you to store old content conveniently. Content on these classes is easily distributable.
• Digital Preservation – Government agencies are moving towards digital preservation where they face data integrity issues. Amazon data storage classes perform regular data integrity checks.

Conclusion

AWS S3 Glacier and AWS S3 Deep archive Glacier facilitates retaining old data at feasible costs. Data is ever-growing. These amazon storage classes make it easier to manage, store and access archive data.

Let us know your thoughts on implementing AWS storage classes at contact.us@virtuetechinc.com.

Amazon S3 vs EFS vs EBS. Which storage option is the best for you?

Amazon Web Services (AWS) is known for its vast number of product offerings. In the storage arena, three of amazon’s most popular services are S3, EFS, and EBS.

The question is, which one to use for your solution?

Fortunately, these services work differently from each other. So, once you have listed down your requirements clearly, it is easy to pick one over the other.

The storage strategy plays a crucial role in determining performance as well as expenditure. We have written up all you need to choose the right one.

Difference between S3 VS EFS VS EBS

·      S3

      It provides simple object storage. It manages data as objects which means all data types are stored in native formats. There is no relation between files. It is used to host images and videos, data analytics, mobile and web applications.

Data objects are distributed across several machines and are accessible from anywhere in the world. Files are managed with the help of metadata.  

·      EFS

     It is an Elastic File Storage system that grows or shrinks as per resource requirements. It is similar to a traditional file storage system. The only difference is data is organized into directories and sub-directories. It is widely used for SaaS applications. You can use the same EFS to several EC2 instances simultaneously. It is fast and accessible. However, it fails to provide the potential to execute complex queries.

·      EBS

    AWS EBS is block-level data storage. It stores files in equal-sized multiple volumes called blocks. These blocks act as separate hard drives. Block storage devices provide higher performance than regular file storage. It is more flexible. You need to initialize EBS on an Amazon EC2 instance.

Characteristics	S3	EFS	EBS
Performance	Supports up to 3500 PUT/LIST/DELETE requests and 5500 GET requests per second   Slower than EBS and EFS	Baseline performance: 3GB/s Upto 7K IOPS       Faster than S3 but slower than EBS	250-500 IOPS per volume for HDD and 16-64K per volume for SSD     Faster than S3 and EFS
Storage and file size limit	No limit on storage. The individual object can be up to 5TB.	Max 16TB per volume Max 52TB for individual file	Max storage 16 TB No file size limit.
Accessibility	Accessible via the internet using APIs. It is accessible from anywhere.	Accessible from any region. It has up to 1000 concurrent EC2 instances. You can share files between different regions on multiple EFS instances.	Accessible via single EC2 instance. Accessible in a particular region.
Access Control	IAM user-based authentication. Uses user policies and bucket policies. Public access via block public access	IAM user-based authentication. Security groups.	IAM user-based authentication. Security groups.
Data backup and restoring	Use versioning or cross-region replication	EFS to EFS replication	Automated backups and snapshots
Cost	Pay per use     Free: 5GB 1st 50 TB/month at $0.023/GB Next 450 TB/month at $0.022/GB Over 500 TB/month at $0.021/GB	Pay per use     Standard storage: $0.30-$0.39 per GB per month (depends on region) Infrequent storage: $0.025-$0.03 per GB per month Provisioned throughput: $6 per MBps-month	Pay for provisioned capacity   Free: 30GB General-purpose: $0.045 per Gb per month Provisioned SSD: $0.125 per GB/month, $0.065 per IOPS/month
Advantages	Easily scalable and can access multiple instances of EC2.   Best used for data archiving	Faster than S3 with high IOPS and low latency   Most scalable due to availability of multiple EC2 instances.   Auto Scaling potential	Faster than S3 with high IOPS and low latency   Cheaper than EFS.
Usage	Use for static storage like for website, media, and entertainment.   Best to use for running analytics over static data pool present in S3 with the provision of running complex queries to draw insights	Best for large quantities of data. Large analytic workloads as in bigData analytics.   Most suitable for a content management system.   Must be used for lift and shift application support	Database backups and low latency applications   Use EBS for high-performance storage service on a single device.   Transactional and NoSQL databases

  IOPS = Input/Output operations per second

Conclusion

Amazon provides you with three options out of which one will match up with your requirement. Use S3 for static purposes. EFS can be used for an application that is highly scalable and high-performance oriented. EBS is best suited where a single device requires high performance. The best example of EBS will be transactional databases.

You can reach out to us and share your thoughts at contact.us@virtuetchinc.com

5 reasons why AWS framework is well architected

Good software has a well-designed infrastructure that is the foundation of any software. It is something that separates resilient software from the rest. It becomes challenging to build a solution that meets your requirement and has a strong infrastructure.

AWS (Amazon web services) provides you with some rock-strong and simple pillars to help you in building technical solutions. Involving these pillars in your design will help you in producing high-performance and resilient software. It will allow you to focus more on other aspects like business requirements.

AWS’s well-architected framework gives you a set of guidelines and benchmarks to build a resilient infrastructure for your applications. It helps in reviewing and improving your cloud infrastructure.

Let us discuss each pillar of AWS’s well-architected framework with their design principles.

1.   Operational Excellence

It enables to deliver business value by supporting development, carrying out workloads, driving insights from operations, and continuous process improvement.

            5 Design principles associated with it are:

• Treat operations as code: Review it, limit human error and enable a consistent response to events.
• Make small, frequent changes that can be easily reversed.
• As workload keeps evolving, refine operation procedures frequently.
•  Anticipate failure: Look for failing scenarios, identify the possible source of them and test your applications on them.
• Learn from operational failure: Keep a record of reasons for operational failure, grow from them and share them with the team.

2.   Security

The 2^nd pillar of the AWS framework encompasses the complete protection of data, systems, and assets while leveraging the benefits of cloud technologies. Use detective controls to identify a security breach.

Its design principles are:

• Implementing a strong identity foundation: Provide minimum required permissions and create separate roles for authentication.
• Enable traceability: Monitor, audit, and alert actions.
• Apply security to all layers: Use multiple security controls to prevent unauthorized attacks.
• Automate security best practices: Automation will help in taking action rapidly without manual intervention. It will help in increasing security even at idle times.
• Protect data in transit and at rest:  Use encryptions, tokenization, and bifurcate data into sensitivity levels.
• Keep people away from data: Remove direct access or manipulation of data.
• Prepare for security events: Implement incident management and investigation protocols.

3.   Reliability

It is the ability to perform intended functions correctly and consistently. It involves operating and testing the workload throughout the lifecycle.

 Its design principles are:

• Automatically failure recovery: Use indicators to trigger when the threshold breaches.
• Test recovery procedures: Use automation to exercise different failure test cases and test recovery procedures.
• Scale horizontally to increase aggregate workload availability: Use a load balancer to assign requests to multiple resources.
• Stop guessing capacity: Automate to add or remove resources based on scalability.
• Manage change in automation: Make the change in infrastructure using automation.

4.   Performance efficiency

It is the fourth pillar of AWS’s well-architected framework. It encompasses the ability to use compute resources judiciously while maintaining performance with variable workloads. It includes continuous monitoring to observe how workload is changing.

Its design principles are:

• Democratize advanced technologies: Assign complex tasks to the cloud vendor.
• Go global in minutes: Deploy your workload in multiple AWS applications and regions.
• Use serverless architecture: Remove physical servers for your computational activities.
• Experiment more often: Use different instances, storage, and configuration to find the best solution for you.
• Consider mechanical sympathy: Use a technical approach that best suits your workload.

5.   Cost optimization

It is delivering business value at the lowest price possible without compromising the quality.

Its design principles are:

•  Implement cloud financial management: It helps you in optimizing the cost.
• Adopt a consumption model: Use the pay-per-usage model for your resources. Add or subtract resources based on business requirements.
• Measure overall efficiency: Monitor the gains you make by increasing outputs and reducing costs.
• Stop spending money on undifferentiated heavy lifting: Let AWS manage data center operations like racking, stacking, and powering the server.
• Analyze and attribute expenditure: Identify usage and cost of the system. It provides transparent attribution of IT costs to each workload owner.

Conclusion:

            The 5 pillars of AWS’s well-architected framework provide architectural best practices to run your application without facing any bottleneck and anomaly. These help you in gaining insights and creating a stable and effective system.

Let us know what you think of this framework and design principles at contact.us@virtuetechinc.com

Starting guide to SAS

SAS (Statistical Analysis Software) was created in 1960 by the SAS Institute. It is used for Advanced Analytics, Data Visualization, and Multivariate Analysis. When it comes to data analytics, SAS software is the first choice for most companies because it offers high-quality services that other analytics tools cannot match. Companies can incorporate SAS analytics easily into their processes because of functionalities, support, and extensive interoperability. SAS is also easy to understand for people with a non-technical background because of its simple visual presentation of information and data.

Are you aware of the standalone reason why SAS has an advantage over others in the Business world? It is because of the data security. It is impossible to extract data without a license. It is one of the reasons where SAS beats the R programming language for analysis. SAS comes with complete data security and thoroughly tested algorithms that make it much more reliable than other data analytics software. Its data management tools, business intelligence, and services help users to convert data into better decisions.

SAS has a large user base – exceedingly more than 4000 companies using SAS. Some of them are Google, Facebook, Twitter, Accenture, etc. Whether it’s IT, healthcare, or banking, companies from every industry have invested in SAS analytics. Despite the availability of alternative platforms for answering the many questions which persist at a business and organizational level, SAS is widely preferred for generating informative reports.

SAS took advantage of the Graphical User Interface (GUI) introduced by the Macintosh with the introduction of JMP (Jump) for statistics. Jump is primarily used for applications such as Six Sigma for design, quality control, engineering, and scientific analysis.

SAS is an independent platform that allows you to run SAS either on Linux or Windows. SAS is driven by SAS programmers who create reports for data analysis using several sequences of operations in the SAS datasets. Over the years, SAS has been supplementing its product line with various solutions. It provides a solution for data governance, data quality, Big Data analytics, text mining. We may presume that SAS has a solution for any business area.

SAS Institute Inc. has released a free SAS University Edition that is good enough for learning SAS programming. It provides all the features that you need to know in BASE SAS programming. It is a widely used core component that contains a data management facility and a programming language for data analysis. It enables you to learn other SAS components too.

REFERENCES:

1. https://www.tutorialspoint.com/sas/sas_overview.htm
2. https://www.newtechdojo.com/overview-of-sas-download-certification/
3. https://www.geeksforgeeks.org/introduction-to-sas-programming/
4. https://seleritysas.com/blog/2019/01/23/sas-software-4-reasons/#:~:text=When%20it%20comes%20to%20data,the%20availability%20of%20alternative%20platforms.

Top IoT Security Challenges and their Solutions

IoT is one of the most common emerging technology. It comes with its benefits and risks. The advocates of the technology promote IoT as a means to make life easier and better. Almost every appliance is IoT-enabled. Today, more than 30% of all network-connected enterprise endpoints are IoT devices.  The IoT market will rise to 1 trillion USD in 2023 from 749 billion USD in 2020.

On the other hand, IT professionals are considering it risky due to user privacy and security issue. According to NETSCOUT Threat Intelligence Report, on average IoT device gets attacked within 5 minutes after it goes live. In 2025, 152,000 IoT devices will be connecting to the internet every minute. How disastrous would it be if a majority of these devices are prone to cyberattacks?

Let’s find out more about these security challenges before it gets too late.

IoT Security Challenges

1. Lack of physical security

Attackers tend to attack physical devices located in remote places for a long time. IoT Device manufacturers must ensure the physical security of the devices. However, building secure and low-cost IoT devices will be a challenging task for manufacturers. In 2020, IoT hardware made up one-third of the malware-infected devices.

2. Botnet attacks

A botnet is a collection of malware-infected machines. Attackers use them to bring down a target by sending thousands of requests per minute. In this way, IoT devices can be transformed into a new botnet. It is possible because IoT devices do not get security updates like computers.

3. Absence of visibility

IoT-enabled devices such as coffee machines, refrigerators, and air conditioning systems are not tracked. As these devices don’t have regular security updates, it serves as an entry point to the network for attackers. Security teams could not do anything if they don’t have complete information of devices connected to the network.

4. Industrial Espionage

Privacy is a grave security issue. IoT devices collect user’s information. Hackers may take over the device and use this personal data against the owner. Privacy issue escalates when hackers can collect corporate data. They can either expose it or sell it.

5.Hijacking

IoT devices with poor security become the target of ransomware. Ransomware encrypts and blocks access to sensitive data. Hackers will ask for ransom for decrypting the files. Though, cases of ransomware are rare in IoT. However, it is the most vulnerable sector at risk in the future.

IoT Security Solutions

Average annual global spending on IoT security measures will reach 631 million in 2021 with a CAGR of approximately 21%. Any device connected to a network is vulnerable to cyberattacks. Let’s try to mitigate these issues with the following solutions.

1. Usage of IoT Security Analytics

Security analytics helps in significantly reducing security issues and potential threats.

It can prevent potential threats by deducing insights from the data collected from various sources. It can also raise an alert over an anomaly in a network by correlating data from different domains. The collection of valuable information helps in the early detection and effective prevention of threats.

2. Endpoint Detection and Response Security

Endpoint detection and response (EDR) security tool helps you in identifying attacks in real-time. It quickly identifies and prevents malicious activities. EDR solutions use IoT data to gain threat intelligence. The threat intelligence can improve the base configuration and deployment of IoT devices in a more secure way.

3. Fortifying Visibility

Every organization must maintain a detailed inventory of all connected devices. It helps in responding to security incidents and taking security-compliant actions.

4. Encrypted Communication

Attackers eavesdrop on the communication to hijack the devices. So, one must encrypt communication between IoT devices and other applications to prevent a data breach. The most common encryptionprotocols are HTTPS, AES 128, and AES 256.

5. Implementing Authentication mechanism

Various authentication mechanisms are available for IoT devices like multi-factor authentication, digital certificates, and biometrics. They can help in reducing the vulnerability of IoT devices.

Conclusion

Securing IoT devices is a collaborative task of the manufacturers as well as users and admins. The manufacturer must focus on encryption and security analytics. And the admins should monitor activities and respond promptly. Let’s decide to turn IoT into the Internet of Secure Things. Share your thoughts about this at contact.us@virtuetechinc.com.

What are the benefits of choosing agile over other software development methodologies?

Every technical organization is shifting towards the agile methodology of software development. Today, it is influencing your work. But before we dive into the role of the agile process, let’s understand what it is.  And, how it got started?

Time before Agile

                        Before the agile process, waterfall model was dominant for software development. It was the only standard followed at that time. It emphasized on documentation before the start of the software development. Coding was started when both functional and technical documentation had completed. Integration, testing, and deployment were completed after coding. This whole cycle takes a lot of time.

Now imagine, in the middle of the development cycle, you forgot to write a crucial specification or you had written a wrong specification. This was normal as coding often highlights the concealed practical aspects. Many times customers were delivered functionality that was of no use or had expected a different outcome. In the waterfall model, all-important feedbacks were taken after the software deployment.

According to a UK study of 1081 projects in 2001, 81% of project failures quoted waterfall as the primary cause.

Rise of Agile

                        The gaps between expectations and results led to industrial frustration in the 1990s. The time lag between requirement and delivery led leaders to think of new development method. That method is today known as Agile.

 Agile is an iterative approach of project management and developing software that delivers little bits of user functionalities at the end of 2-4 week cycles called iterations or sprints. Every sprint consists of small functionally working requirements called user stories. At the end of a sprint, mutually agreed user stories will be delivered to the client. Customer feedback is considered and incorporated into future sprints. This backlog is called sprint backlog. In 2008, a survey by Voke inc. took feedback about agile. 90% of survey takers claimed agile improved their flexibility. 84% of respondents said agile improved project visibility. 

Roles of agile

User:

The agile process keeps the user or customer in the center of the development that was missing in the traditional waterfall model.

Product Owner:

A product owner is an internal stakeholder who acts as the voice of the user. His/her responsibilities are to define a product vision and help the development team to make it happen.

He often divides complete product vision into user stories and prioritizes them according to the customer’s requirements. He becomes the anchor who resolves any problem faced by the software development team.

Software development team:

                                    Teams in agile comprise diversely skilled people. It is because agile focuses on delivering a working model (user story). To test, integrate and deploy end-to-end working user story team needs to be multidisciplinary. One can directly communicate with the customer at any  time for queries and issues.

Four core values of the Agile

• More Focus on People and Interaction

Agile focuses more on people and interaction as they are vital for collaboration and problem-solving. Best tools and processes are worthless if they are in the wrong hands.

• Working Software

Agile emphasizes working functionality than detailed documentation. Agile is more product-centric than other process-centric methodologies.

• Customer Collaboration

The customer is more involved in an agile process. He/she will review the deliverable at the end of a sprint. He/she addresses the queries of the development team throughout the process.

• Responding to change

Changing requirements and priorities lead to the success of agile. A dynamic roadmap is mandatory that can vary from sprint to sprint, sometimes even from month to month.

Benefits of Agile

Increased customer’s involvement, continuous delivery of sprints throughout the year, and an adaptable customer-centric approach lead to the following benefits:

1. High-quality products
2. User-focused testing
3. Greater customer satisfaction
4. Reduced risks of project failure
5. Better tracking of project’s progress
6. Better project predictability
7. Faster return on investment
8. The speedy and flexible process increases productivity

Please reach out to us for any of your business requirements for Agile methodology at contact.us@virtuetechinc.com