Unsupervised Machine Learning with AWS

Introduction

To explore AWS unsupervised machine learning, let us first understand what unlabelled data is. Data that have not been tagged with identifying labels, tags, or classifications are called unlabelled data. Using a machine-learning algorithm to analyse and cluster unlabelled datasets is called unsupervised learning. It is also known as unsupervised machine learning. It identifies concealed patterns & data groups in the data that helps in making cross-selling strategies, recognising an image, and defining customer segmentation.

Unsupervised Learning with AWS

Unsupervised learning can be implemented using AWS Glue & Amazon Athena. The data is present on the Amazon S3 cloud instance. AWS Glue takes the data as input and applies K-means clustering to segregate the data into 100 different clusters based on some attributes.

Amazon Athena then comes into play. Athena is used to launch queries on the data. It will fetch you result based on the parameters you have used in Athena’s query. Athena produces the result. Both AWS Glue & Amazon Athena works on their own without any managing servers.

Tasks associated with AWS unsupervised learning

There are three tasks associated with unsupervised learning.  We will explore each of them along with common algorithms and approaches to conduct them effectively and efficiently.

1.    Clustering

Clustering is a data mining technique that groups unlabeled data based on similarities or differences. It is used to process raw unclassified data into groups based on patterns or structures in the information. Some common clustering algorithms are:

·      Exclusive Clustering:

This approach states that one data point can belong to only one cluster. It is also regarded as hard clustering. K-means clustering is an example of exclusive clustering where every data point is divided into K groups. The K represents the number of groups based on distance from each group’s centroid.

·      Hierarchical Clustering

It follows two approaches: the top-down and bottom-up approach. Data points are isolated as different groups and then combined iteratively based on the similarities until one cluster remains.

·      Probabilistic Clustering

It is an unsupervised technique that solves density estimation or ‘soft’ clustering problems. Data points are aggregated based on their belonging to a particular distribution.

2.    Association

It is a rule-based method for identifying the relationship between variables in a given dataset. It helps companies to find associations between products. It facilitates understanding consumer behavior to improve cross-selling strategies. Amazon’s people recently bought these items together is an example of association.

Apriori Algorithm

This algorithm helps in market basket analyses. It is applied to the data that changes frequently. It is also used with the collection of items that have a high probability of purchasing a product given purchase of another product.

3.    Dimension Reduction

Generally, more training data improves the performance of the machine learning model but it makes dataset visualization difficult. This technique is used when the number of dimensions is higher. It reduces massive volume of data into a manageable size. This method is generally used in preprocessing data stage. Different dimension reduction methods are:

• Principal component analysis: It reduces redundancies and compresses datasets using linear transformation to create a new data representation.
• Autoencoders: It takes advantage of the neural network to compress data and create a new representation of the original datasets.

Applications of AWS Unsupervised Learning

Machine learning has been widely used to improve user experience and to test systems for quality assurance. Unsupervised learning allows businesses to take leverage of patterns and structures found in large volumes of data. Some of the applications of AWS unsupervised learnings are:

1. Recommendations: Recommendations of any kind are the outcome of unsupervised learning. In real-time, its applications are recommended products on e-commerce, news, articles, services, etc.
2. Medical Imaging: Unsupervised learning is used to diagnose patients quickly and accurately. It is also helpful in image detection, classification, & segmentation.
3. Anomaly Detection: Identifying outliers in the given datasets will detect the anomaly in data. It could be due to human error, faulty equipment, or a security breach.           
4. Data Mining

Conclusion

We have learned about unsupervised learning, how it works. AWS unsupervised learning has numerous applications in real world. AWS had launched applications to assist in the implementation of unsupervised learning.

Please get in touch with us at contact.us@viruetechinc.com to share your thoughts with us on this and any of your related requirements.

Emerging data engineering trends in 2022

With the explosion in cloud adoption, enterprises will continue to focus on digital transformation. Many companies are moving data, using analytics, and exporting business use cases to the cloud. Cloud adoption and cloud migration will continue to gain momentum in 2022 with the introduction and acceptance of DataOps, 5G, and edge analytics playing key roles in the digitisation journey. Let us see what we can expect in 2022 in the field of data engineering.

1.   Increase in adoption of DataOps

Organizations will practice DataOps to improve data quality and reduce insight deriving time. DataOps helps in building and delivering trusted, consumption-ready data pipelines to the data analytics team. Most businesses use different tools for data ingestion, data preparation, and pipeline orchestration. Hence, there is a high demand to automate data flow and manage pipelines using a single dashboard.

In 2022, DataOps will come into implementation from on-paper research. DataOps methodologies combined with the pipelines will increase agility and achieve business value faster. Organizations will learn to implement DataOps in their existing multi-cloud and hybrid environment.

2.   Improved analytics with launch of 5G

In recent years, edge computing has evolved significantly. However, its adoption was low due to latency limitations. With the launch of 5G worldwide, this limitation is no longer present. Data computation and storage will move close to the source, and communication between edge devices will occur at superfast speed. It will help in collaborating analytics for real-time decision-making. Hence, cloud service providers have started to provide edge computing services.

3.   Migration to hybrid, multi-cloud, and edge environments

According to a report by Gartner, investment in public cloud services will grow from $396 billion in the last year to $482 billion in this year. Enterprises are looking forward to more hybrid, multi-cloud, and edge environments are paving the way for new distributed cloud models.

Companies adopting the hybrid, multi-cloud model will see a boost in speed and agility, reduction in complexity and costs, and strengthening of cybersecurity. McKinsey predicts that 70% of the companies will use hybrid, multi-cloud models by the end of the year 2022. The rise in these models is due to the increase in unstructured data. To provide more value to the customers, an organization can no longer use traditional batch-based reporting. Companies must build their infrastructure to overcome the unstructured data challenge while ensuring compliance and security regulations.

4.   The great convergence of technologies and services

In 2022, we will see an overlap of technologies implemented in real-time scenarios. These overlaps will include Artificial intelligence, Business intelligence, and Machine learning use cases. Experts predict the convergence of data warehouses and data lakes that will be highly beneficial. It will simplify technologies and vendor landscape.

The organization often collects data from multiple tools and platforms. So, a robust metadata strategy will be required to regulate data processes for higher customer value. Whether no-code/low-code or highly sophisticated structures platforms, a solution that empowers companies to organize data and create a robust architecture will be of high importance in the year 2022.

5.   Increased democratisation

After the pandemic, there will be an increase in no-code digital solutions. The rise of no-code/low code will drive greater agility with automation. The organization will move from code-centric-workflows to self-service analytics that allows a non-technical person to become a key player in the ecosystem. These democratized data workflows will ease access to data and make smarter business decisions. Unstructured data will increase manyfold in the coming years. So, ubiquitous architecture will be of great importance. The architecture that enables access to complicated datasets and uses them across various tools and platforms will be in high demand.

Conclusion

            Convergence of platforms and services, introduction and adoption of hybrid, multi-cloud, implementation of DataOps with the improved infrastructure of 5G, and increased democratisation are some of the revolutionary changes in data engineering that are expected in the year 2022. Share your thoughts with us at contact.us@virtuetech.com on how these innovations will help you.

Best practices for implementing the AWS

AWS has taken up the market by storm. Today, AWS accounts for 33.8% of the market share globally. No wonder, AWS is the market leader. AWS has provided a well-architecture framework. Five key pillars of the AWS framework are:

To leverage the key advantages of the robust architecture of AWS, best practices must be followed. Let us discuss some of the best practices that everyone must know.

1.   Protecting AWS credentials

Your AWS account signifies a business relationship between you and AWS. You would use an AWS account to manage all AWS resources and services. For managing this, full access must be given to the account that aggravates the security risks.

One of the best practices would be to create one or more AWS Identity and Access Management (IAM) users and give them the necessary permissions to manage the daily interactions.

2.   Safeguarding Applications

Access to your AWS applications to the outside world should be given when it is necessary. For making an application exclusive to a particular group (Or IPs), a security group should be created for that web server. This security group can be used to restrict based on IP & ports through which the application is accessed. Entry to your server should be forbidden for all other internet traffic.

3.   Backup & Recovery

Backup and recovery are proactive measures to mitigate different issues that might occur during and after application deployment. For that case, one must be prepared with a backup plan. Some checkpoints regarding data backup and recovery are:

• Regularly backup your amazon instance using available AWS tools.
• Deploy sensitive components in such a manner that they are available through multiple availability zones. Also, replicate the data periodically so that data can be recovered in case of application crashing.
• Watch and respond to the events
• Prepare a strategy to handle failure. You can attach a network interface for replacement instances.
• Test your recovery process for instances and Amazon EBS volumes.

4.   Use of trusted Advisor

A trusted advisor provides 4 services that help in saving money, improving system performances, reliability, and closing security gaps. These services are:

• Service Limit check
• Security Groups – Port and IP check
• Internet and Access management use check
• Multifactor Authentication on the root account.

To check the current status of the above four services, click on the trusted advisor icon on the AWS console under Administration & Security. It is one of the easiest best practices for implementation.

5.   Understanding AWS shared responsibility model

AWS believes security is a shared responsibility between AWS and the Customer. It segregates the responsibilities of the customer and AWS. Awareness of this business model helps the user in taking necessary steps that help to improve upon security and compliance.

The customer is responsible for instance configuration, firewall, and management tasks. Server-side & client-side encryption, data integrity authentication, & network traffic protection are other duties of the customer.

Also, make sure that you apply security to all layers. Build a virtual firewall to control and monitor network traffic to secure your infrastructure.

6.   Configure password policy & use password generator

A strong password policy is a must for the security of the organization. Password cracking, brute force attacks are some of the most common security attacks. Creating a password policy determines password creation, modification, and deletion rules.

Apart from the password policy, password generators must be used to generate a complex secure password. It helps you in creating a relatively complex password.

Conclusion

Before setting up AWS infrastructure for your organisation, one should be thorough with the best practices of AWS. Implementing best practices will consume time but it is a proactive step that will help in mitigating issues and attacks. This article speaks of some of the best practices one can implement. Let us know through comments what best practices you have applied in your AWS.

Share your thoughts on this and AWS requirements (if any) with us at contact.us@virtuetechinc.com and we will reach out to you to help your business in the best possible manner.

Opening new boundaries with next-level alternate Data Sources

Businesses are evolving to be data-driven. To convert data into implementable insights, you will need to connect to data to analyse. Data sources are a physical or digital location where data is stored in form of a table, object, etc. The data sources are the first thing you require to import or connect before building anything else.

Let us understand what are the different types of data sources.

1.   Databases

A relational database is one of the most common sources of data sources. Each database represents an individual data connection. Generally, data is stored in table format in relational databases where the column depicts the attribute and the row shows the value of the attributes. The important task would be to prepare and connect the database to your software.

2.    Flat Files

It involves preparing, uploading, & updating the CSV file. Flat files like CSV files are easy to use. They can be easily uploaded to the required software. They can be used to query, visualise and pair up with other data sources. When a flat file is imported into a system, it is broken down to detect column data types. Before you upload the flat file, it must be decided that if you want to upload the file once or update the file regularly. Different ways to upload a flat-file are:

1. Upload the flat file directly from the computer
2. Create an uploading link on cloud storage services like OneDrive, Dropbox, Google Drive, etc.

3.   Social Media

Social Media has recently emerged as a data source as information from it helps to understand customer preferences and choices. Connecting to social media data is an easy task. It can be connected to Google Analytics effortlessly. Custom connections to social media platforms can be created to connect to them.

4.   APIs and other platforms

Many online platforms and web services, such as Google Analytics and Facebook, reveal data through APIs. One needs a custom connection to import data from these services to their software.

5.   Database Access

Full database access can be given to allow your software to access the database. Proper firewall settings must be implemented. Also, software must be compatible to connect to the database. There are two methods of establishing a connection with the database:

1. Direct Method: It is one of the easiest methods to connect to a database. The only concern is software and database must be compatible with each other.
2. SSH Tunnel Method: It is a much safer option than the direct method. Data is transported using an SSH tunnel over an encrypted SSH connection.

6.   Join Data Sources

You can join multiple data sources and perform cross-database queries to fetch the desired data. Extracting data from unified sources has proved to be beneficial to get actionable insights from the data. You can link flat files or databases to one another to perform advanced calculations on data stored in various locations.

DSN: Common anchor to all the data sources

DNS stands for Data source name. It is a file that contains information on the required digital data source. It consists of the name of targeted data table. It is used in the case of all digital data. 

Conclusion

Data is very crucial for businesses as their decisions depend on it. Identifying data sources is an important task as it affects the decision-making process.

Share your data sources’ requirements with us at contact.us@virtuetechinc.com and we will reach out to you to help your business in the best possible manner.

Emerging Trends in AWS Cloud

Ever since the launch of AWS in 2016, it has managed to maintain its dominance in the cloud sector. Credit goes to its expanding infrastructure and continuously evolving applications. They have explored a lot of fields and come up with several features to maintain a better customer satisfaction rate.  As a result, AWS revenue totaled $16 billion that is 39% more than the previous year’s revenue.

With the outbreak of the Coronavirus crisis, the need to migrate to the cloud has increased manifolds. Hence, investments in cloud infrastructure have gone up significantly. These factors with key business upgrades led AWS to work on innovation in all emerging sectors. Let us explore what new advancements we can expect in the future.

AWS Trends in 2021

1.    Cooperative Cloud Service Provider:

Coming years will see the beginning of joint cloud vendors collaborations. Vendors can collaborate to speed up business upgrades, utilize shared resources, and take on AWS trends.

Multi-cloud Analysis is another boon of collaboration. It includes deployment, tracking, system allocation, and redistribution of workload. Multi-cloud insights are used by several organizations to ease cloud management, develop resilience, and execute better multi-cloud strategies.

2.    Expansion in application of AI & ML

Advance research on AI & ML is continuing to expand. Besides basic research, the scope of AI & ML is growing as big as exploring a collection of research papers.

Cloud seems to be the best fit for AI & ML. As it can help with heavy computational demands and can support collection, sharing, and storing data on large scale.

Amazon SageMaker, AWS lake formation, and AWS Glue are some of the cloud solutions that can extract information from data stored in Amazon S3 (a simple storage service). These services build metadata, query it, and analyze results using advanced AI and ML frameworks.

3.    Amazon DevOps Guru

It is one of the latest AWS trends. This service helps in boosting the efficiency and availability of the programmer. It identifies the activities that show unusual behavior and spot it out so that you can take care of organizational problems before it affects your customers.

4.    Connecting to Big Data & IoT

Both Big data and IoT are selling like hotcakes and companies are looking for a way to combine these technologies. Big data processes and analyzes data of a particular entity. IoT embedded in specialized tools with valid data can be used for industrial purposes.

If big data and IoT use the cloud, a company can maximize its performance. It will also help in deriving fruitful insights and making good strategic choices.

AWS has launched AWS IoT Greengrass, an open-source IoT cloud service that lets you develop, deploy and manage applications.

5.    Amazon DocumentDB:

Amazon DocumentDB is another cutting-edge feature of AWS. It is a fast, scalable, and fully manageable document database service which is MongoDB compatible. It also allows you to use your existing MongoDB server and tools. DocumentDB facilitates database management tasks such as setup, configuration, backup, or scaling. It increases the efficiency of MongoDB by 100%.

Conclusion

AWS is known for coming up with innovative solutions. It is leaving no stone unturned in coping up with the new advancements. We have discussed the recently emerging trends where AWS is launching new services. These new services can be easily integrated with upcoming technologies like AI, ML, IoT, & Big Data.

Reach out to us at contact.us@virtuetechinc.com for sharing your thoughts on the rising trends in AWS Cloud and we will help you find out its scope in your business.

Why is the need for Data Engineering on a rise?

According to a recent report published by ResearchandMarkets.com, the data engineering market is expected to witness a growth at a CAGR of 16.3% in the period from 2021 to 2026. Data engineering according to Gartner is “the methodology of making the appropriate data accessible and available to the data consumers (which include data scientists, data analysts, business analytics and business users).”

Data storage and processing used to be the primary challenges, not a very long time back. The emergence of Cloud have transformed both storing and processing of data into assets or commodities. This extensively helped the teams to concentrate on bigger problems such as efficiently handling of metadata management, integration of various data systems, tracking and realising a high data quality. Today, as more organisations look at revamp their analytics environments, their use of data engineering to drive better business insights is on the rise.

To better understand, data engineering comprises of the task of making raw data usable to data scientists and groups in an organisation. It also includes a number of specialities of data science. Data engineering also creates a analyses of providing  predictive models and exhibit short- and long-term trends.

Data engineers lend a helping hand to data scientists and data analysts find the right data, make it accessible in their environment, ensure the data is credible and that sensitive data is hidden, functionalize data engineering pipelines and also ensure lesser time is spent on the preparation of data.

Enterprises must choose a platform and AI-driven approach for end-to-end data engineering instead of stitching together piecemeal solutions. The platform must also boost technologies like cloud, Spark, serverless, and Kafka that have led to the emergence of data engineering.

1. Find the right dataset with an intelligent data catalogue.
2. Bring the appropriate data into your data lake or ML environment with mass ingestion.
3. Functionalize your data pipelines with enterprise data integration.
4. Process real-time data at scale with AI-powered stream processing.
5. Shield confidential information with intelligent data masking.
6. Safeguard trusted data to be available for insights with intelligent data quality.
7. Streamline data prep and enable collaboration with enterprise-class data preparation.

HOW CAN WE HELP?

In this article, we looked at the top insights in the data engineering market as well as some of the existing data engineering challenges. Now, let us see how VirtueTech can take care of and add value to your data engineering needs.

• Our solution can help your business to strengthen your ‘Data As a Service’ capability and transform big data pipelines into robust systems prepared for business analytics.
• Our team is committed to provide you the access to the right format of data at the right time across your enterprise.
• Our solutions not just only help to accelerate the integration of analytics into your business process, but also reduce the time and complexity, as well as ensure compliance with security and privacy requirements. This can assure that your business adapts effortlessly to new technological changes.
• We also offers an integrated approach to collect, store, govern, and analyse data at any scale for driving a successful data engineering initiative in your organization.

CONCLUSION

The world today is investing hugely on data science to draw maximal insights and leverage its benefits. Therefore, what we must always remember is optimizing and improving the data science processes. Data engineering services facilitate existing data science solutions and adds value to the business by saving costs and time.

Write to us at contact.us@virtuetechinc.com sharing your thoughts on data engineering and we will help you find out the scope of data engineering in your business.

Data Science vs. Data Engineering

The surfacing of machine learning, artificial intelligence, natural language processing, and other emerging technologies has boosted the adoption of data science. Companies are trying to manage massive amounts of data produced daily using big data. 

With the rise in big data, the roles of data science and data engineering are increasing significantly. Often, they are used interchangeably. These roles may sound the same but are very different. Let us walk through the roles one by one.

Data Scientist

Data Science is an advanced level of data analysis driven by computer science and machine learning. Their job starts with data preprocessing where they clean, understand, and try to fill data gaps. They play a crucial role in setting up businesses. They look out for a recent market problem and use data analysis and processing to provide the best solutions. They analyze, process, and model data to achieve business objectives. The models created by them are valuable in extrapolating, analyzing, and finding patterns in the existing data.  They have to have skills in computer science, statistics, and mathematics.

Data Engineer

Data engineers transform data from multiple sources into a single format. They help in building up systems that collect, manage, and convert raw data into useful information for data scientists and business analysts. Data Engineers build a data pipeline to facilitate data flow from one system to another. They help in cloud data integration, solving complex data problems, and address data plumbing issues.  Their primary roles are to clean the data, compile and integrate database systems, scale to multiple systems, write complex queries, and strategize disaster recovery systems.

Data Engineers: Lesser-Known Cousin of Data Scientist

Data engineers are the less famous cousin of data scientists who are equally important. They prepare data infrastructure for analysis. They work upon things like format, resilience, security, and scaling of data. They focus on collecting the data and validating the information that data scientist uses to solve the problems.

Their primary focus is to build data pipeline by using big data techniques with real-time analytics. They also focus on writing complex data extraction queries so that data is easily accessible.

A large amount of data is managed over distributed networks. So, they must have a solid knowledge of the Hadoop system along with common scripting languages such as PostgreSQL, MySQL, etc.

Nowadays, many data-intensive projects such as e-commerce sites, financial networks use Artificial Intelligence. These projects created the role of data engineer to be of utmost importance.

In gist, roles of data engineers are:

• Build, test optimal data pipelines
• Automate manual processes
• Optimize data delivery
• Re-design current infrastructure for improved scalability

Data Scientist: The Ubiquitous Role

The role of data scientist has been projected as mandatory for all innovative technology projects. They focus on understanding human functions of vision, speech, language, decision-making; designing machines and software to imitate these processes. They are responsible for finding the best model for tasks like replacing complex decision-making processes, automating customer interaction keeping it as natural as possible, etc. They are responsible for conducting detailed market and business research that helps in identifying trends and opportunities.

 They should have a sound knowledge of emerging technologies and model-building techniques. Data visualization and design thinking are also crucial for them. Typically, having good knowledge of R or python framework with one or more deep learning frameworks (such as TensorFlow) and distributed data tools (such as spark) is required.

Major roles of a data scientist can be summed up as:

• Develop custom data models and algorithms
• Build tools and processes to improve performance and data accuracy
• Use predictive modeling to optimize targeting, revenue generation, customer experiences
• Develop a framework for testing and model quality checking

Conclusion

The demands of both data engineers and data scientists are very high in demand. Both positions are equally paid around $100,000 per year. Their ever-growing demand has opened up doors of a new field called ‘Computational data science’ where data engineering is equally emphasized with AI concepts.

The data scientists dig into the research and visualisation of the data while data engineers take care of data flowing correctly from the pipeline. Both are equally essential and have a huge demand with limited supply. Choosing either one of them should be considered a great choice. They both work together, complementing one another to help businesses attain their goals.

Share your thoughts on data engineering vs. data science with us at contact.us@virtuetechinc.com.

Data Engineering Key Skills and Tools

Data engineering is a science that helps to make data beneficial and usable for its consumers. In other words, data engineering helps create raw data analyses to provide predicting data models to exhibit short and long-term trends. A recent Gartner report indicates that in the year 2020, over 80% of the companies will be working on the Cloud platforms, and of this, over 40% would go for public cloud platforms. A recent webinar depicted how more than 60% of the businesses in Canada were forced to expedite their technology plans like Cloud migration primarily due to the global pandemic. In the year 2021, and “Data Lake” is rapidly evolving as a rapid shift from on-premises servers to the Cloud at a startling speed.

Big Data skills are significant in data engineering-related job roles. This stands true from everything related to designing, creating, building, and maintenance of data pipelines to collecting raw data from varied sources, and then consequently optimizing data for performance. Data engineering professionals perform plenty of tasks to seek an understanding and knowledge of big data frameworks, databases, infrastructures, data containers, and a lot more.

Here, let us discuss five very important tech skills that one must have to succeed in their data engineering career journey –

1. Data warehousing – A data warehouse is a system that helps companies in organizing, and analyzing big data in a meaningful manner. Data Warehouses are central repositories of streamlined and integrated data from diversified sources. These data sources can be ERP Software, CRM solutions,s or accounting software. Businesses utilize this data to create reports, perform data analysis and data mining to achieve useful insights.
2. AI and Machine learning – A knowledge of the AI terminology has today become a significant skill in a data engineer job role. Incorporating machine learning into big data can certainly advance the process by discovering the data engineering trends and patterns. M/L algorithms can be used to identify the incoming data and patterns and transform them into insights. An awareness of machine learning needs a solid foundation in mathematics and stats, and programming languages like Python, and Cloud-based tools like AWS Sage maker.
3. Data Pipelines – Data transformation, which is done to ensure the efficient development of the “Data Lake” data for analyzing and visualizing it in the future, is another significant skill set required for data engineering. Additionally, processing real-time streams, data warehouse queries, JSON, CSV, and raw data is a daily affair. Knowledge of tools like Apache Kafka, Amazon Web Services (AWS), Cloud Development Kit (CDK) is also a must-have skill for a data engineer.
4. Programming Language – JAVA, Python, and Scala are some very popular languages for data engineers. Python helps with statistical analysis and modeling. And, Java is used to work with data architecture frameworks. These languages are preferred by programmers the most as it helps them write maintainable, reusable, and complex functions. These languages are efficient, versatile, suitable for text analytics, and provide a strong foundation for data engineering services and big data support.
5. Database Tools – Data storage, organization, and management are crucial for data engineering job roles. The two kinds of commonly used databases are SQL-based and NoSQL-based. SQL-based databases are MySQL and PL/SQL that are used to store structured data. Whereas, NoSQL-based databases such as MongoDB, Cassandra, and others can be used to store big volumes of structured, unstructured, and semi-structured data as per the requirements of the application.

In this article, we looked at the skills and tools that are required in the data engineering market today. Now, let us take a look at how VirtueTech can take care of your data engineering needs.

Data engineering services facilitate existing data science solutions and add value to the business by saving costs and time. With the right set of skills and tools, data engineers can essentially become more rewarding. VirtueTech makes data engineers more efficient and data consumers more confident. This is how VirtueTech helps companies get more value from their data.

Share your data engineering requirements with us at contact.us@virtuetechinc.com and we will get back to you with our value addition.

Why is the need for Data Engineering on the rise?

According to a survey report by ResearchandMarkets.com, the data engineering market is expected to observe growth at a CAGR of 16.3% over the forecast period of 2021-2026. Gartner explains data engineering as “a methodology of making the appropriate data accessible and available to various data consumers (including data scientists, data analysts, business analytics and business users).”

Not a very long time back, merely data storage and processing were dominant challenges. The emergence of the Cloud has transformed both storing and processing of data into assets or commodities. This largely allowed and helped the teams to concentrate on bigger problems such as efficiently handling metadata management, integration of various data systems, tracking, and realizing a high data quality. Today, as more organizations look at modernizing their analytics environments, their use of data engineering to drive better business insights is on the rise.

To understand it in simple words, data engineering involves the task of making raw data used by data scientists and groups in an organization. It also includes a number of specialties of data science. Data engineering also creates an analysis of providing predictive models and exhibit short- and long-term trends.

Data engineers help data scientists and data analysts find the right data, make it accessible in their environment, ensure the data is credible and that sensitive data is hidden, functionalize data engineering pipelines, and also ensure less time is spent on data preparation.

Enterprises must opt for a platform and AI-driven approach for end-to-end data engineering instead of stitching together piecemeal solutions. The platform must also support technologies like cloud, Spark, serverless, and Kafka that have led to the emergence of data engineering.

1. Find the right dataset with an intelligent data catalog.
2. Bring the appropriate data into your data lake or ML environment with mass ingestion.
3. Functionalize your data pipelines with enterprise data integration.
4. Process real-time data at scale with AI-powered stream processing.
5. Shield confidential information with intelligent data masking.
6. Safeguard trusted data to be available for insights with intelligent data quality.
7. Streamline data prep and enable collaboration with enterprise-class data preparation.

In this article, we looked at the data engineering market insights and some of the existing data engineering challenges. Now, we will also take a look at how VirtueTech can take care of and add value to your data engineering needs.

• Our solution can help your business to power up your ‘Data As a Service’ capability and turn big data pipelines into robust systems prepared for business analytics.
• Our team works dedicatedly to enable access to the right format of data at the right time across your enterprise.
• Our data engineering solutions cannot only help to accelerate the integration of analytics into your business process, but also reduce time and complexity, and ensure compliance with security and privacy requirements. This can assure that your business can effortlessly adapt to new technological changes.
• We also offer an integrated approach to collect, store, govern, and analyze data at any scale for driving a successful data engineering initiative in your organization.

The world today is focusing on data science to draw insights and leverage the benefit from the data. What we forget is to optimize and improve the data science processes. Data engineering services facilitate existing data science solutions and add value to the business by saving costs and time.

Share your thoughts on data engineering with us at contact.us@virtuetechinc.com and we will help you find out the scope of data engineering in your business.

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.