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A Complete Guide to Data Management: Best Practices and Strategies in 2023. Java, Java Developer Philippines
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A Complete Guide to Data Management: Best Practices and Strategies in 2023
A Complete Guide to Data Management: Best Practices and Strategies in 2023 650 486 Exist Software Labs

Data management is a critical aspect of modern businesses and organizations. With the exponential growth of data in today’s digital world, effectively managing and utilizing data has become a crucial factor for success.

However, DM can be complex, involving various processes and strategies to ensure data accuracy, integrity, security, and usability.

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In this comprehensive guide, we will delve into the world of data management, covering best practices, strategies, and tools to help you harness the power of data and make informed decisions.

In today’s digital world, data has become one of the most valuable assets for businesses and organizations. Proper DM is essential for ensuring data accuracy, integrity, confidentiality, and availability, while also enabling organizations to make informed decisions and gain insights from their data.

We will cover the fundamentals of DM, including the key concepts, best practices, and challenges involved in handling data effectively.

Whether you’re a business owner, data professional, or simply interested in learning more about data management, this guide will provide you with a solid foundation to understand the importance of data management and how to implement it in your organization.

Key Concepts of Data Management: 

Data management encompasses a wide range of activities, from data collection and storage to data analysis and interpretation. Here are some key concepts that form the foundation of data management:

  1. Data Governance: Data governance involves defining policies, standards, and procedures for managing data across an organization. It includes establishing roles and responsibilities for data management, ensuring data quality, and complying with regulatory requirements.
  2. Data Lifecycle: The data lifecycle consists of different stages, including data creation, data capture, data storage, data processing, data analysis, and data archiving or deletion. Understanding the data lifecycle is critical for effectively managing data at each stage of its life.
  3. Data Quality: Data quality refers to the accuracy, completeness, consistency, and reliability of data. Ensuring data quality is crucial for making informed decisions based on accurate and reliable data.
  4. Data Security: Data security involves protecting data from unauthorized access, alteration, or destruction. Data breaches can have severe consequences, including financial loss, damage to reputation, and legal liabilities. Implementing proper data security measures is essential to safeguard sensitive data.

Best Practices for Effective Data Management

Implementing best practices can help organizations ensure that their data is managed effectively. Here are some key best practices for DM:

  1. Define Data Management Policies: Establishing clear DM policies, including data governance policies, data quality policies, and data security policies, is critical for guiding data-related activities in an organization. Policies should be documented, communicated, and enforced consistently. 
  2. Create a Data Inventory: Creating a data inventory helps organizations identify and catalog their data assets, including data sources, data types, data owners, and data usage. This helps in understanding the scope of DM and enables effective data governance. 
  3. Implement Data Quality Controls: Implementing data quality controls, such as data validation, data profiling, and data cleansing, helps ensure that data is accurate, complete, and consistent. Data quality controls should be applied at different stages of the data lifecycle to maintain data integrity. 
  4. Secure Data Access: Implementing proper data access controls, such as role-based access controls (RBAC) and data encryption, helps ensure that only authorized users have access to data. Regularly review and audit data access permissions to prevent unauthorized access. 
  5. Backup and Disaster Recovery: Implementing regular data backup and disaster recovery procedures is essential to protect data from loss due to hardware failure, software malfunction, natural disasters, or other unforeseen events. Test and validate backup and disaster recovery procedures to ensure data recoverability.

Challenges in Data Management

Data management is not without its challenges. Some of the common challenges in DM include:

  1. Data Complexity: Data comes in various formats, structures, and volumes, making it challenging to manage and analyze effectively. Organizations must deal with different data sources, data integration, and data transformation to ensure data consistency and accuracy. 
  2. Data Privacy and Compliance: Data privacy regulations, such as GDPR and CCPA, impose strict requirements on organizations to protect personal data and comply.

As we reach the middle of 2023, DM continues to be a critical aspect of any organization’s success. With the increasing importance of data in decision-making, it is essential to have proper data management practices and strategies in place.

Furthermore, organizations should develop a DM strategy that aligns with their business goals and objectives. This strategy should include data storage, data access, data sharing, and data retention policies.

In conclusion, with the increasing importance of data, organizations must prioritize data management best practices and strategies to derive value from their data and gain a competitive advantage in their industry.

Data Ingestion, Data Integration, Data Quality,Driven Organization
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The Importance of Data Ingestion, Data Integration, and Data Quality in becoming a Data-Driven Organization.
The Importance of Data Ingestion, Data Integration, and Data Quality in becoming a Data-Driven Organization. 650 486 Exist Software Labs

Data ingestion, integration, and quality are crucial steps in becoming a data-driven organization:

Ingesting, storing, organizing, and maintaining the data generated and gathered by an organization is known as data management. A key component of implementing IT systems that power business applications use to deliver analytical data to support operational decision-making and strategic planning by corporate executives, business managers, and other end users is effective data management.

Data management is a collection of many tasks that aims to guarantee correct, accessible, and available data in business systems. The majority of the work is done by the IT and data management teams, but business users also contribute.

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These are the key steps in transforming a company into a data-driven organization.

What is Data Ingestion, Data Integration, and Data Quality?

  1. Data Ingestion: It is the process of acquiring data from various sources and bringing it into a centralized data repository for analysis and reporting. Without effective data ingestion, data silos can form, making it difficult to access and integrate data across the organization.

 

It involves acquiring data from different sources, such as databases, cloud storage, or even manual input, and ensuring that the data is transformed and formatted in a way that can be easily integrated and analyzed.

 

  1. Data Integration: This process merges data from different sources into a unified view, making it easier to analyze and make informed decisions. Lack of data integration can lead to inconsistencies, duplications, and errors in data analysis.

 

This step requires removing duplicates, resolving conflicts, and transforming data into a consistent format so that the data can be used effectively for analysis and decision-making.

  1. Data Quality (Cleansing): Cleaning data ensures that it is accurate, consistent, and free of errors. Poor data quality can negatively impact decision-making and hinder the effectiveness of data analysis.

 

The data quality process involves validating the data, correcting errors, and removing inconsistencies, to ensure that the data is trustworthy and fit for its intended use. These three steps are crucial for organizations to effectively leverage their data to make informed decisions, drive business growth, and achieve their goals.

By focusing on data ingestion, integration, and quality, organizations can ensure that they have a solid foundation for their data analysis and decision-making processes. It enables organizations to gain valuable insights, make informed decisions, and ultimately drive business growth and success.

Next is How to Operationalize the Data in a data-driven organization:

 

  • Establish a clear data strategy: The first step is to create a clear data strategy that aligns with the overall business strategy. This strategy should define the business problems that data can help solve, the data sources to be used, the tools and technology required, and the KPIs that will be used to measure success.

 

  • Identify data requirements: Determine what data is required to support the business strategy and goals. This involves identifying the types of data needed, the sources of data, and the frequency of data collection and updates.

 

  • Collect and process data: Collect the relevant data and process it in a way that makes it usable for analysis. This may involve data cleaning, normalization, and transformation.

 

  • Analyze data: Use analytics tools and techniques to analyze the data and derive insights that can inform business decisions. This may involve descriptive analytics, predictive analytics, and prescriptive analytics.

 

  • Communicate insights: Communicate the insights to stakeholders in a way that is clear and actionable. This may involve creating dashboards, reports, or visualizations that highlight the key findings and recommendations.

 

  • Integrate insights into operations: Use the insights to inform business operations and decision-making processes. This may involve integrating insights into existing workflows, processes, and systems.

 

  • Monitor and evaluate: Monitor the impact of the data-driven initiatives and evaluate the success against the KPIs identified in the data strategy. Make adjustments as needed to improve performance.

Overall, operationalizing data in a data-driven organization requires a culture that values data-driven decision-making, a commitment to continuous improvement, and the right technology and tools to support data collection, analysis, and communication.

 

Big Data, Data Solutions, Healthcare, Retail
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Trends and Industries: How Data Solutions upend existing sectors to new heights in 2023?
Trends and Industries: How Data Solutions upend existing sectors to new heights in 2023? 650 486 Exist Software Labs

The defining era of data is currently upon us. Business model threats and economic shocks are common. Power is changing wherever you look, including in the market, our technological infrastructure, and the interactions between companies and customers. Change and disruption have become the norm. Data Solutions have been useful in innovating the industry.

Data-savvy businesses are well-positioned to triumph in a winner-take-all market. In the past two years, the distance between analytics leaders and laggards has increased. Higher revenues and profitability can be found in companies that have undergone digital transformation, embraced innovation and agility, and developed a data-fluent culture. Those who were late to the game and who still adhere to antiquated tech stacks are struggling, if they are even still in operation.

So, when you create your data and analytics goals for 2023, these are the key trends to help you stay one step ahead of your competitors.

Healthcare

Data Analytics and Data Solutions can be used to improve patient outcomes, streamline clinical trial processes, and reduce healthcare costs. 

Some specific examples of how Analytics is being used in healthcare include:

  1. Improving patient outcomes: Analytics can be used to identify patterns and trends in patient data that can help healthcare providers make more informed decisions about treatment plans. For example, data from electronic health records (EHRs) can be analyzed to identify risk factors for certain conditions, such as heart disease or diabetes, and to determine the most effective treatments for those conditions.
  2. Streamlining clinical trial processes: Data Analytics can be used to improve the efficiency of clinical trials by allowing researchers to identify suitable candidates more quickly and by helping them to track the progress of trials more closely.
  3. Reducing healthcare costs: Analytics can be used to identify inefficiencies in healthcare systems and to help providers implement cost-saving measures. For example, data analysis can be used to identify patterns of overutilization or unnecessary testing, and to develop strategies for reducing these costs.

Financial services

Data Analytics can be used to detect fraud, assess risk, and personalized financial products and services. 

Some specific examples of how Data Analytics is being used in the financial industry include:

  1. Fraud Detection: Data Analytics can be used to identify patterns and anomalies in financial transactions that may indicate fraudulent activity. This can help financial institutions to prevent losses due to fraud and to protect their customers.
  2. Risk Assessment: Analytics can be used to assess the risk associated with various financial products and services. For example, data analysis can be used to assess the creditworthiness of borrowers or to identify potential risks in investment portfolios.
  3. Personalizing financial products and services: Analytics can be used to gain a deeper understanding of individual customers and to personalize financial products and services accordingly. For example, data analysis can be used to identify the financial needs and preferences of individual customers, and to offer customized financial products and services that are tailored to those needs.

Retail

Retail companies can use Data Analytics to optimize pricing, understand customer behavior, and personalize marketing efforts. 

Some specific examples of how Data Analytics is being used in the retail industry include:

  1. Prizing Optimization: Retail companies can use Data Analytics to identify patterns in customer behavior and to optimize their pricing strategies accordingly. For example, data analysis can determine the most effective price points for different products and identify opportunities for dynamic pricing (i.e., adjusting prices in real time based on demand).
  2. Understanding customer behavior: Analytics can be used to gain a deeper understanding of customer behavior and preferences. This can help retailers to make more informed decisions about the products and services they offer, and to identify opportunities for cross-selling and upselling.
  3. Personalizing marketing efforts: Analytics can be used to deliver more personalized and targeted marketing efforts to customers. For example, data analysis can be used to identify customer segments with similar characteristics and to develop customized marketing campaigns for each segment.
  4. Cost Reduction: Being able to have a JIT (Just in Time) procurement and storage of items which in turn increases/optimizes warehouse capacity and reduces spoilage, and improves logistics.

Manufacturing

Data Analytics can be used to optimize supply chain management, improve production efficiency, and reduce costs. 

Some specific examples of how Data Analytics is being used in the manufacturing industry include:

  1. Optimizing supply chain management: Analytics can be used to improve the efficiency of the supply chain by identifying bottlenecks and inefficiencies, and by developing strategies to address these issues.
  2. Reducing fuel consumption: Analytics can be used to identify patterns in fuel consumption and to identify opportunities for fuel savings. For example, data analysis can be used to identify the most fuel-efficient routes or to identify vehicles that are consuming more fuel than expected.
  3. Improving fleet management: Analytics can be used to improve the efficiency of fleet management by identifying patterns in vehicle maintenance and repair data, and by helping fleet managers to develop strategies to optimize vehicle utilization and reduce downtime.
  4. Forecast roadworthiness of vehicles: This can help set trends on when a vehicle would break down or need repairs based on utilization, road conditions, climate, and driving patterns.

Energy

Data Analytics can be used to optimize the production and distribution of energy, as well as to improve the efficiency of energy-consuming devices.

Some specific examples of how Analytics is being used in the energy industry include:

  1. Optimizing the production and distribution of energy: Analytics can be used to optimize the production and distribution of energy by identifying patterns in energy demand and by developing strategies to match supply with demand. For example, data analysis can be used to predict when energy demand is likely to be highest and to adjust energy production accordingly.
  2. Improving the efficiency of energy-consuming devices: Analytics can be used to identify patterns in energy consumption and to identify opportunities for energy savings. For example, data analysis can be used to identify devices that are consuming more energy than expected and to develop strategies to optimize their energy use.
  3. Monitoring and optimizing energy systems: Analytics can be used to monitor and optimize the performance of energy systems, such as power plants and transmission grids. Data analysis can be used to identify potential problems or inefficiencies and to develop strategies to address them.

Agriculture

Analytics can be used to optimize crop yields, improve the efficiency of agricultural processes, and reduce waste.

Some specific examples of how Data Analytics is being used in agriculture include:

  1. Optimizing crop yields: Analytics can be used to identify patterns in crop growth and to develop strategies to optimize crop yields. For example, data analysis can be used to identify the most suitable locations for growing different crops and to develop customized fertilization and irrigation plans.
  2. Improving the efficiency of agricultural processes: Data Analytics can be used to identify patterns in agricultural data and to develop strategies to optimize processes such as planting, fertilizing, and harvesting.
  3. Waste Reduction: Analytics can be used to identify patterns in food waste and to develop strategies to reduce waste. For example, data analysis can be used to identify the most common causes of food waste on farms and to develop strategies to address those issues.

These are just a few examples of the many industries that are likely to adopt Data Analytics technologies as part of their digital transformation efforts in the coming years. 

Other industries that are also likely to adopt Analytics Technologies include Government, Education, and Media, among others. In general, Data Analytics Technologies are being adopted across a wide range of industries because they can help organizations to gain insights from their data, make more informed decisions, and improve their operations. 

As more and more organizations recognize the value of Analytics, it’s likely that we’ll see even greater adoption of these technologies in the coming years.

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Data Science, Science and Technology
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Data Science 101: What are concepts you need to know before entering the Data Science world?
Data Science 101: What are concepts you need to know before entering the Data Science world? 650 486 Exist Software Labs

I was playing around with data and then I found the Science — Yes, my introduction to the world of Data Science has been a part of my research work.

If you’re like me, starting out with Data Science looking for resources that can give you a jump start or at least a better understanding of it or you have just heard/read the term being coined and want to know what it is, of course, you can find a gazillion materials about it, this is, however, how I started and got familiar with the basic concepts.

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What is ‘Data Science’?

Data Science provides meaningful information based on larger amounts of complex data or big data. Data-Driven Science combines different fields of work in statistics and computation to interpret data for decision-making purposes.

Understanding Data Science

How do we collect data? — Data is drawn from different sectors, channels, and various platforms including cell phones, social media, e-commerce sites, various healthcare surveys, internet searches, and many more. The surge in the amount of data available and collected over a period of time has opened the doors to a new field of study based on big data — the huge and massive data sets that contribute towards the creation of better operational tools in all sectors.

The continuous and never-ending access to data has been made possible due to advancements in technology and various collection techniques. Numerous data patterns and behavior can be monitored and it can make predictions based on the information gathered.

In technical terms, the above-stated process is defined as Machine Learning; in layman’s terms, it may be termed Data Astrology — predictions based on data.

Nevertheless, the ever-increasing data is unstructured in nature and is in constant need of parsing in order to make effective decisions. This process is really complex and very time-consuming for organizations — and hence, the emergence of Data Science.

A Brief History / Background of Data Science

The term ‘Data Science’ has been in existence for about three decades now and was originally used as a substitute for ‘Computer Science’ in the 1960s. Approximately 15–20 years later, the term was used to define the survey of data processing methods used in different applications. 2001 was the year when Data Science was introduced to the world as an independent discipline.

Disciplinary Areas of Data Science

It incorporates tools from multiple disciplines to gather a data set, process and derive insights from the data set and interpret it appropriately for decision-making purposes.

Some of the disciplinary or noteworthy areas that make up the Data Science field include Data Mining, Statistics, Machine Learning, Analytics Programming, and the list goes on. But, we would be doing a brief discussion mainly on the aforesaid topics as the concept of Data Science mainly revolves around these basic concepts, just to keep it simple.

Data Mining applies algorithms to complex data sets to reveal patterns that are then used to extract useful and relevant data from the set.

Statistics or Predictive Analysis uses this extracted data to gauge events that are likely to happen in the future based on what the data shows happened in the past.

Machine Learning can be best described as an Artificial Intelligence tool that processes massive quantities of data that a human is incapable of doing in a lifetime — it perfects the decision model presented under predictive analytics by matching the likelihood of an event happening to what actually happened at a predicted time in the past.

The process of Analytics involves the collection and processing of structured data from the Machine Learning stage using various algorithms. The data analyst interprets, converts, and summarizes the data into a cohesive language that the decision-making team can understand.

Data Scientist

Literally speaking, the job of a Data Scientist is multi-tasking: We collect, analyze and interpret massive amounts of structured and unstructured data, and in a maximum number of cases, to improve an organization’s operations. Data Science professionals develop statistical models that analyze data and detect patterns, trends, and various relationships in data sets.

This vital information can be used to predict consumer behavior or to identify business and operational risks. Hence, the job of a Data Scientist can be described as a story-teller that uses data insights in telling a story to the decision-makers in a way that is understandable. The role of a Data Scientist is becoming increasingly important as businesses rely more heavily on data analytics to drive decision-making and lean on automation and machine learning as core components of their IT strategies.

Present & Future of Data Science

Data Science has become the real thing now and there are potentially hundreds and thousands of people running around with that job title. And, we too have started seeing these Data Scientists making large contributions to their organizations. There are certainly challenges to overcome, but the value of data science from a business point of view is pretty clear at this point.

Now, thinking about the future, certain questions definitely arise — “How will the practice of data science be changing over the next five years? What will be the new research areas of data science?”

“Will the fundamental skills remain the same?”

These are certainly debatable questions, but one thing is for sure — inventions have happened and will continue to happen when there arises any demand for the betterment of the future. And, the world would keep benefiting from data science through its upcoming innovations.

The possibilities of how to utilize Data Science in real-world scenarios are endless! Our Data Solutions team would be happy to help you capitalize on this technology for your enterprise.

Exist Software Labs Inc, Informatica Data Governance Pocket Session
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Exist Software Labs Inc and Informatica Pocket Session 2022: Realizing Data Governance Benefits in a Cloud-Hybrid World
Exist Software Labs Inc and Informatica Pocket Session 2022: Realizing Data Governance Benefits in a Cloud-Hybrid World 650 486 Exist Software Labs

Exist Software Labs Inc and Informatica Pocket Session: Realizing Data Governance Benefits in a Cloud-Hybrid World

On September 15, Exist Software Labs, in a joint effort with Informatica, gathered various market leaders from various verticals to conduct another pocket session on Data Governance and its benefits in a Cloud-Hybrid World.

Exist and Informatica: Realizing Data Governance Benefits in a Cloud-Hybrid World

Jon Teo, Data Governance and Privacy Expert at APJ spoke at the event about its benefits. He demonstrates how it helped various industries such as healthcare, automotive, insurance, manufacturing, power, and others around the world by leveraging its risk and compliance to protect the enterprise, as well as data intelligence that unlocks more value and data opportunities for businesses.

According to him, rapid cloud adaptation and a hybrid ecosystem generate more volume from more sources, making it difficult to discover, manage, and control data, requiring the urgent need for an agile governance approach.

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Kingsley Dsouza, a Technical Data Governance Privacy Domain Expert, was one of the speakers who also demonstrated Informatica’s services. According to him, “Data Governance platform helps users in finding information that will assist them in solving their day-to-day business problems, which most organizations struggle with and take a long time to process.”

It’s no secret that the Asia-Pacific region lags behind the rest of the world in data management, with less than 50% of organizations having standardized data management capabilities. As the amount of data generated in the region continues to grow at an exponential rate, organizations are scrambling to find effective ways to manage and store all of this information, which is where the agile governance approach comes into play.

Mitigate security risks and ensure compliance with data privacy laws by standardizing your data management! Get in touch with our team to know more.

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    Exist Software Labs Inc. and Informatica
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    Exist Software Labs Inc. and Informatica held a joint Pocket Session on Intelligent Data Management Cloud at the Shangri-La Fort Hotel in BGC!
    Exist Software Labs Inc. and Informatica held a joint Pocket Session on Intelligent Data Management Cloud at the Shangri-La Fort Hotel in BGC! 650 486 Exist Software Labs

    Exist Software Labs Inc. and Informatica held a joint Pocket Session on Intelligent Data Management Cloud at the Shangri-La Fort Hotel in BGC!

    ‘Data is the new oil. Like oil, data is valuable, but if unrefined it cannot really be used. It has to be managed/processed (integrated, mapped, transformed) to create a valuable entity which provides insights that drives profitable activities.’ – Informatica

    A collaboration with Informatica


    Exist Software Labs inc collaborated with Informatica for an exclusive face-to-face event last July 28, 2022, at the Shangri-La Fort Hotel in BGC. The guests were able to meet with data management expert and Informatica’s Head of Cloud Product Specialist, Daniel Hein, who shared how companies can bridge the gap between technology and business through automation, integration, and data governance, unlocking true business value from data.

     

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    The world is changing, and so are your business’s needs. You must be able to adapt quickly to keep up with the changes. “In the last two years, a lot has changed. We are faced with new ways of doing business; the world is moving to a data-driven digital economy… However, there are CONSTRAINTS that you must overcome.” says Daniel Hein, Head of Cloud Product Specialists, APAC and Japan.

    That is why businesses must change their approach. The new Intelligent Data Management Cloud intends to help clients with that! The first and most comprehensive AI-powered data management solution in the industry. A single cloud platform. Every cloud-native service you’ll ever need for next-generation data management.

    IDMC

    Meet the new Intelligent Data Management Cloud of Informatica!

    IDMC platform cuts through red tape and provides accurate AI models across your organization so you can make timely decisions based on the most up-to-date information.

    It also gives you 360-degree views of your data across all areas of your business—so you can see who has access and what they’re doing with it—and allows easy workflow management.

    It is built on top of an enterprise cloud platform; and is equipped with a powerful security model that helps keep sensitive information secure from hackers.

    If you’re looking for a way to help your company prepare for this transition and stay competitive in an ever-changing marketplace, look no further! We specialize in helping companies not only to keep pace but also to improve their bottom line through digital transformation.

    Download our FREE DATASHEET!

    Begin your journey toward data maturity.
    and transform into a data-driven organization today!

    DOWNLOAD DATASHEET

    Download FREE Datasheet!

      Get Your Free Data Sheet

      YugabyteDB
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      YugabyteDB in 2022: What Is Good For?
      YugabyteDB in 2022: What Is Good For? 800 507 Exist Software Labs

      Why YugabyteDB?

      The database evolved from the network & hierarchical model of the 60s to what eventually became the dominant force in databases in the 70s up to the present time—the relational model.

      From this model arose the big names in Relational Database Management Systems like DB2, Oracle, and Sybase. In the 80s, a player emerged that would forever change the RDBMS landscape: Postgres.

      Postgres is the only RDBMS to have won DB of the year 3 times: 2017, 2018, & 2020. This speaks of the universal trust that developers have placed in the system and this has only increased with the rise of open-source software and cloud-native computing.

      Which begs the question: What is cloud-native computing?

      To put it simply, the rise in the services-based model championed by the major cloud providers like AWS, GCP, & Azure has given way to a new way of developing applications, with key emphases on scalability, resilience, high availability, and agile deployments.

      Combining the relational supremacy of Postgres and cloud-native computing, we are then faced with the next step in the evolution of the RDBMS—Distributed SQL— and YugabyteDB is the No. 1 Distributed SQL platform today.

      Use Cases

      What is YugabyteDB good for?

      I will mention 4: cloud-native applications, applications requiring massive scale, geo-distributed workloads, and traditional RDBMS modernization.

      1. Cloud-native applications – Build stateful microservices by leveraging modern cloud-native frameworks such as GraphQL, or in any language of your choice like Django, Spring, Go, etc.

      2. Massive-scale applications – Seamlessly deploy IoT and streaming services that demand high throughput, support for large data sets, and/or many concurrent connections.

      3. Geo-distribution – Move data closer to users for resilience, performance, and compliance with the industry’s largest choice of deployment and replication models.

      4. RDBMS modernization – Evolve Oracle, SQL Server, and DB2 workloads to a modern distributed SQL database with minimal disruption. If you can migrate to PostgreSQL, you can migrate to YugabyteDB.


      Real-World Projects

      We have deployed YugabyteDB in several government and educational institutions that employed cloud-native applications development—requiring user connectivity from various geo-locations—along with the migration of MySQL databases.

      YugabyteDB has delivered in all of these projects, requiring very minimal tweaking in existing SQL code and even speeding up queries in many instances.

      YugabyteDB will very soon be the de facto RDBMS of choice in a cloud-native world.

      Exist is your data solutions partner of choice!

      Explore the next level of your digital transformation journey with our Data Solutions Services. Let’s look at opportunities to better maximize your ROI by turning your data into actionable intelligence. Connect with us today, and we’ll proudly collaborate with you!

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      Energy Tech Trends, Java, Java Philippines
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      2022 Energy Tech Trends to watch
      2022 Energy Tech Trends to watch 800 507 Exist Software Labs

      The COVID-19 pandemic, numerous heavy typhoons, and other unfortunate events affect all business sectors in the country. One of the major industries affected is Energy and Utilities, and it highlighted the necessity for The Energy Industry to adopt a sustainable perspective and improve the technology system.

      Energy Tech Trends 2022

      With the rising demand for energy in 2022, the Department of Energy will continue its advocacy to produce renewable energy to cut market prices and achieve sustainability. On the other hand, the private sector will continue to develop technology systems to achieve efficiency, and high effectiveness and keep up with the continuously evolving energy sector.

      While numerous technology solutions can assist these firms in achieving their digital objectives, only a few are expected to have a significant influence in 2022. These are the 2022 Energy Tech Trends to watch!

      1. Powering Digital Economy Through IoT (Internet of Things)

      IoT, or the Internet of Things, has played a vital role in advancing digitization in several industries, including IT, energy, agriculture, healthcare, and many more.

      It is one of the most advanced technologies, and one of its advantages is that it improves the efficiency of several businesses, including energy. And as for the energy sector, one of the most important functions of IoT is energy conservation. 

      The Internet of Things enables electricity firms to read data in real-time. It enables them to quickly gather, calculate, and analyze data to improve decision-making. It also helps the energy industry transform into an integrated system, resulting in a smart solution that is equipped with advanced technology to increase industry value and maintain asset efficiency for the benefit of the economy.

      2. Fifth-Generation Technology will establish the connection

      Many companies will continue to improve their systems in 2022. The sector will continue to advance the electric grid to make it more reliable and less expensive, thanks to the national government’s directive to push for renewable energy and industry’s developing market demands.  

      These companies rely on Millions of connected devices and digital systems, such as smart meters, sensors, management systems, to communicate data from many locations. And with their objective to digitize their system, they need to have fast and dependable technology, thus creating a need to access 5G technology. 

      5G technology is the next generation of cellular technology after 4G. It has faster speed, lower latency, and the capacity to connect more devices at the same time.

      Fifth-generation wireless technology will provide new features and more efficient smart grids. New 5G mobile networks will assist the integration of unconnected devices into new smart grids; it will also help the development of new electricity load forecasting software for accurate energy monitoring and forecasting. Organizations will now be able to receive and process the massive volume of data at quicker speeds with no chance of downtime.

      3. Companies in the energy industry will continue to migrate to the cloud

      The cloud holds the potential for endless growth, system efficiencies, and digital integration in any business industry.

      With the Power industry’s continued growth, it needs a system that can handle its complex process and massive data efficiently, effectively, and precisely; this is where the cloud can help.

      The cloud has the potential to change every aspect of the energy value chain. Connectivity, scalability, analytics, and automation can all help you save money and increase profits in countless ways.

      Thanks to Exist’s strong foundation in the power industry, with its business solutions to industry market leaders and cloud services to other business verticals, we can now quickly apply tailored advancements to your company.

      4. Artificial Intelligence will revolutionize the game.

      Artificial Intelligence (AI) is becoming relevant in the energy industry and has great potential for future energy system structures.

      Digital technologies such as Artificial Intelligence (AI) will make energy sector systems more intelligent, efficient, dependable, and sustainable, which benefits the entire energy sector chain, from generation to transmission to distribution to the consumer.

      In terms of alignment with the government’s ambitions, AI would also benefit Renewable Energy. With the growing use of renewable energy sources, it is becoming increasingly difficult to regulate the megawatts that are fed into the grid; with this, power networks will be unstable and prone to blackouts.

      With this technology, renewable energy sources may now provide real-time, accurate data that allows AI to predict capacity levels.

      5. Energy Sector will embrace the power of Machine Learning

      With Machine Learning (ML), it’s as if you have a sophisticated human mind monitoring your system, complete with advanced self-learning algorithms, taking your data to a whole new level by making human-like decisions based on current AI data.

      ML employs approaches that can be applied to predictive maintenance. Power lines, machines, and stations, in essence, are outfitted with sensors that capture operating time series data.

      With enough data, your system can now forecast if a failure will occur in your system, allowing you to more efficiently monitor maintenance, reduce downtime, and avert system failure as soon as possible; thus, lowering your system expenditures.

      6. Taking Advantage of Big Data

      It’s one thing to get your data, but it’s quite another to use it to your advantage. 

      Big Data Analytics has the potential to be a key driver in achieving optimal company performance in the energy sector.

      Big data can help the energy business in many ways, including improved supply chain management, enhanced customer satisfaction, optimizing business efficiency, analyzing future risks and possibilities, and much more.

      As a result, more and more energy companies are becoming more competitive. A superior business strategy that incorporates a large amount of data and efficient processes is assisting them in developing company value and increasing customer satisfaction.

      So, how can you achieve the advanced system and follow Energy Tech Trends?

      Whether you like it or not, the energy sector will continue to advance its technological innovation.

      In this regard, it is important to look for an innovative partner who can add value to your organization, and this is where Exist Software Labs can assist you! With our extensive experience in the energy industry, we could bring you the innovation you deserve.

      Empower your system today!

      Learn how to fully automate your processes to create a more competitive, transparent, and efficient system.
      Take your power system to the next level!

      Learn More
      Exist Data Solutions: The Elephant Behind the Excellence. Java, Java Philippines
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      Exist Data Solutions 2022: The Elephant Behind the Excellence
      Exist Data Solutions 2022: The Elephant Behind the Excellence 650 486 Exist Software Labs

      The preceding year, 2021, was an eventful year for EXIST Data Solutions: new team members were added, new technologies were learned, and new projects were implemented.

      In the enterprise database front, PostgrEX was implemented in a prestigious 5-star hotel and casino, a state university, and a major security agency handling the biggest mall in the country.

      YugabyteDB, the No. 1 cloud-native, distributed SQL database in the world, was implemented in 3 government projects, and premium EnterpriseDB support was rendered to the country’s primary energy market corporation.

      On the Exist data solutions front, Greenplum was also successfully implemented in 3 government projects, thereby enabling these entities to turn their data into actionable insights.

      But what do all these business-transforming technologies have in common? In a word: Postgres.

      Postgres is the database engine upon which PostgrEX, YugabyteDB, EDB, and Greenplum are based. With most of them, modifications in varying degrees were done to core Postgres to deliver a product that is still Postgres, but better!

      As indicated in the article, Databases in 2021: A Year in Review, the dominance of Postgres in the year 2021 was undeniable:

      The conventional wisdom among developers has shifted: PostgreSQL has become the first choice in new applications. It is reliable. It has many features and keeps adding more.

      In 2010, the PostgreSQL development team switched to a more aggressive release schedule to put out a new major version once per year (H/T Tomas Vondra). And of course, PostgreSQL is open-source.

      PostgreSQL compatibility is a distinguishing feature for a lot of systems now.

      Such compatibility is achieved by supporting PostgreSQL’s SQL dialect (DuckDB), wire protocol (QuestDB, HyPer), or the entire front-end (Amazon Aurora, YugaByte, Yellowbrick). The big players have jumped on board.

      Google announced in October that they added PostgreSQL compatibility in Cloud Spanner. Also in October, Amazon announced the Babelfish feature for converting SQL Server queries into Aurora PostgreSQL.

      One measurement of the popularity of a database is the DB-Engine rankings. This ranking is not perfect and the score is somewhat subjective, but it’s a reasonable approximation for the top 10 systems.

      As of December 2021, the ranking shows that while PostgreSQL remains the fourth most popular database (after Oracle, MySQL, and MSSQL), it reduced the gap with MSSQL in the past year.

      Another trend to consider is how often PostgreSQL and Exist Data Solutions is mentioned in online communities. This gives another signal for what people are talking about in databases.

      What does all this mean for you and your business? It means you can entrust your most mission-critical applications to Exist Data Solutions, Postgres, and its derivatives.

      It means you can break free of vendor lock-in and redirect cost savings to core business initiatives. It means your company can be a better version of itself–a more profitable version–in the year 2022!

      Be a Data-Driven Organization.

      An organization that is data-driven recognizes the value of data and bases decisions on factual information. This organization has invested time and money to acquire Data Solutions Services that can source data from both inside and outside the company.

      If your organization is like the majority, you’re seeking methods to accomplish more with less. However, you don’t want to spend a fortune to get the information you require.

      When you need data analytics services or an enterprise-grade database, Exist Software Labs Inc.’s Data Solutions services can open up new possibilities for you.

      Contact us and find out how EXIST Data Solutions can meet all your database-related requirements.

      Exist is your data solutions partner of choice!

      Explore the next level of your digital transformation journey with big data and analytics. Let’s look at opportunities to better maximize your ROI by turning your data into actionable intelligence. Connect with us today, and we’ll proudly collaborate with you!

      Data SolutionsContact Us
      A Fully Dockerized MySQL to YugabyteDB Migration Strategy Using pgloader, Java, Java Philippines
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      A Fully Dockerized MySQL to YugabyteDB Migration Strategy in 2022 Using pgloader
      A Fully Dockerized MySQL to YugabyteDB Migration Strategy in 2022 Using pgloader 768 487 Exist Software Labs

      YugabyteDB Migration Strategy


      While there have been many who began their journey to relational databases with the simple and popular MySQL, the evolution of business use cases involving more than read optimization and the need for more performant, full-fledged, read/write-optimized OLTP systems have given rise to a widespread migration from MySQL to Postgres.

      Along with this, the transition from monolithic to cloud-native has also paved the way for distributed SQL systems that allow for read/write functionality in every node of the database cluster (while maintaining ACID-compliance across all nodes) and cloud-agnostic deployments of these nodes across geographic zones and regions. This is the future of the database, a future where reliability, accessibility, and scalability are built into the product. The future of the database is YugabyteDB.
       

      From MySQL to YugabyteDB—fast!

      The method that we will be using to migrate a MySQL database to YugabyteDB is through the use of pgloader, a very reliable tool for migrating from MySQL (even SQL Server) to Postgres. We will first migrate the MySQL database to a Dockerized Postgres instance using Dockerized pgloader.

      Once the MySQL database has been migrated to Postgres, we will then use the ysql_dump utility that comes with every installation of YugabyteDB to dump the Postgres database into a YugabyteDB-friendly format. This is one of the very useful traits of ysql_dump: it ensures that your Postgres dump can be fully restored in a YugabyteDB instance.

      After getting the dump, we will restore this dump in the blank YugabyteDB database that we’ve created beforehand, thereby completing the migration from MySQL to YugabyteDB!

       

      Steps

      1. Get the Postgres Docker container

      docker run -e POSTGRES_HOST_AUTH_METHOD=trust -p 5432:5432 -d postgres:11

      2. Create the MySQL database counterpart in Dockerized Postgres

      CREATE DATABASE <db name>;

      3. Run Dockerized pgloader to load from MySQL to Dockerized Postgres

      docker run --rm --name pgloader dimitri/pgloader:latest pgloader --debug mysql://<user name>:<password>@<ip address of MySQL DB server>:3306/<source database name> postgresql://postgres@<ip address of Dockerized Postgres>:5432/<destination database name>

      *If a user error is encountered, make sure the user and IP address combination indicated in the error is created in the MySQL source and has access to the databases to be migrated.”

      4. Since pgloader creates a Postgres schema using the database name and puts the tables there, we can change the schema name to “public”

      DO LANGUAGE plpgsql
     $body$
     DECLARE
     l_old_schema NAME = '<schema name>';
     l_new_schema NAME = 'public';
     l_sql TEXT;
     BEGIN
     FOR l_sql IN
     SELECT
          format('ALTER TABLE %I.%I SET SCHEMA %I', n.nspname, c.relname, l_new_schema)
     FROM pg_class c
          JOIN pg_namespace n ON n.oid = c.relnamespace
     WHERE
     n.nspname = l_old_schema AND
     c.relkind = 'r'
     LOOP
     RAISE NOTICE 'applying %', l_sql;
     EXECUTE l_sql;
     END LOOP;
     END;
     $body$;

      5. In this example, we will be using Dockerized Yugabyte as the destination (also applies to other form factors)

      a. 1-node cluster with no persistence: 

      docker run -d --name yugabyte  -p7000:7000 -p9000:9000 -p5433:5433 -p9042:9042 yugabytedb/yugabyte:latest bin/yugabyted start --daemon=false

      b. With persistence:

      docker run -d --name yugabyte  -p7000:7000 -p9000:9000 -p5433:5433 -p9042:9042 -v ~/yb_data:/home/yugabyte/var yugabytedb/yugabyte:latest bin/yugabyted start --daemon=false

      6. Go inside the Yugabyte container

      a. To access the interactive terminal of the container:

      docker exec -it <yugabyte container id> /bin/bash

      b. Go to the bin directory:

      cd /home/yugabyte/postgres/bin

      c. Make sure destination database exists in YugabyteDB:

      CREATE DATABASE <destination yugabytedb name>;

      d. Dump the database in the Postgres container:

      ./ysql_dump -h <ip address of Postgres container> -U postgres -d <database name of postgres db> -p 5432 -f <dump name>.sql

      e. Restore the Postgres dump in the blank database in the YugabyteDB instance:

      ./ysqlsh -p 5433 -d <database name of destination yugabyte db> -f <dump name>.sql

       

      And there you have it! You have successfully migrated your MySQL database to the future of the database. You have migrated to YugabyteDB!

      yugabytedb migration

      Exist is your data solutions partner of choice!

      Explore the next level of your digital transformation journey with big data and analytics. Let’s look at opportunities to better maximize your ROI by turning your data into actionable intelligence. Connect with us today, and we’ll proudly collaborate with you!

      Data SolutionsContact Us
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