Enterprise Database

Big Data, Data Solutions, Healthcare, Retail

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.

Data Science, Science and Technology

Data Science 101: The concepts you need to know before entering the Data Science world.

Data Science 101: The 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.

What is ‘Data Science’?

Data Science provides meaningful information based on larger amounts of complex data or big data. Data Science, or if you would like to say 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

Data Science incorporates tools from multiple disciplines in order 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 the complex data-sets to reveal patterns that are then used to extract useful and relevant data from the set.

Statistics or Predictive Analysis use this extracted data to gauge events that are likely to happen in 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.

Feel free to contact us through this link: https://exist.com/data-solutions/

web 800x507 A Fully Dockerized MySQL to YugabyteDB Migration Strategy Using pgloader 768x487 1

A Fully Dockerized MySQL to YugabyteDB Migration Strategy Using pgloader

A Fully Dockerized MySQL to YugabyteDB Migration Strategy Using pgloader 768 487 Exist Software Labs

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 YugabyteDBfast!

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!

Enterprise Database

The Future of the Database: YugabyteDB

The Future of the Database: YugabyteDB 768 487 Exist Software Labs

The journey to application modernization brought about by the cloud-native renaissance continues, and the benefits to be had are truly being enjoyed by the enterprises that embrace the path. Speed, scalability, resiliency, and agility may seem to just be industry buzzwords, but in reality, they translate to better application deployment, performance, and availability, which further translate to what really matters: happy customers.

This has given way to the concomitant need for databases to adapt to this need for speed, scalability, resiliency, and agility. The way traditional databases have implemented a single-node access to the database cluster via the master node has proven untenable in a commercial environment wherein the need to scale users, not just locally, but across the regional and geographical divide, has become dire and ubiquitous.

This is where the gap is filled by YugabyteDB.

 

What is YugabyteDB?

What is YugabyteDB?

YugabyteDB is a transactional, distributed SQL database that was designed primarily to possess the virtues of the cloud-native philosophy. Its creators wanted a chiefly OLTP database that was fast, easy to add more nodes to, able to tolerate node failures, upgradable without incurring any downtime, and deployable in all form factors (public/private cloud, VMs, and on-prem).

Being a distributed SQL database, it has automatic distribution of data across nodes in a cluster, automatic replication of data in a strongly consistent manner, support for distributed query execution so clients do not need to know about the underlying distribution of data, and support for distributed ACID transactions.

It is a multi-API database that exposes the following APIs (more will be added in the future): 

  • YSQL – an ANSI SQL, fully-relational API that is completely compatible with PostgreSQL 11.2
  • YCQL – a semi-relational SQL API that is based on the Cassandra Query Language

It is a Consistent and Partition Tolerant (CP) database in that in the event of a network partition within the database cluster wherein one of the nodes cannot communicate with the other nodes and determine majority membership, data consistency over availability is prioritized by the system and this node will not be able to accept writes, whereas the nodes that are still part of the majority will remain unaffected.

It is completely open source, released under the Apache 2.0 license.

 

What are the key benefits of YugabyteDB?

The following are some of the benefits that are immediately enjoyed “out-of-the-box”:

  • No single point of failure given all nodes are equal
  • Distributed transactions across any number of nodes
  • Scale write throughput linearly across multiple nodes and/or geographic regions.
  • Low-latency reads and high-throughput writes.
  • Strongly consistent, zero data loss writes.
  • Cloud-neutral deployments with a Kubernetes-native database.
  • Automatic failover and native repair.
  • 100% Apache 2.0 open source even for enterprise features.

In other words, you get a cloud-native, transactional, distributed SQL database system that allows you to read and write on every node in the cluster (with ACID assurance), distribute your application load across many nodes in many regions and geographies, read and write data fast, deploy anywhere, and be highly available—all in open source!

 

Use Cases

YugabyteDB is perfect for:Use Cases of YugabyteDB

Just this morning, social media personality, James Deakin, posted on his FB wall about a particular bank whose “app feels like it’s running on windows 95” (his own words). He ended up closing his account due to the overall poor customer experience brought on by the subpar performance of this bank’s client-facing, internet applications, along with other concerns.

YugabyteDB is perfect for the client-facing, Internet, transactional application.

Want to know more about the Yuggernaut of Distributed SQL? Contact us.

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!

web 800x507 postgrEX 768x487 1

Introducing PostgrEX: How to Fulfill Your Database SLAs Without Having to Sell a Kidney

Introducing PostgrEX: How to Fulfill Your Database SLAs Without Having to Sell a Kidney 768 487 Exist Software Labs

In a past blog post, I gave the definition of software as being enterprise-grade in the following manner:

A piece of software is enterprise-grade when it caters to the needs of not a single individual, nor a select group of individuals, but the whole organization. When applied to database management systems, an enterprise database is an implementation of database software that serves the organization by managing their humongous collection of data. It must be robust enough to handle queries from hundreds to tens of thousands of users at a time. It must also have a host of features that are geared towards improving the productivity and efficiency of the organization, such as multi-processing, parallel queries, and clustering, to name a few.

To tease it out a little bit further, I would like to propose that a database implementation is “enterprise” when it possesses the following attributes:

1.    A database engine that has proven itself in a multitude of business applications globally in a span of decades

2.     Able to meet strict SLAs (at least 5 nines) through high availability and failover mechanisms

3.    Monitoring

4.    Backup and Recovery Management

5.    Connection Pooling

Traditionally, enterprise database implementations have been costly investments and organizations have been willing to pay the price given the criticality of data to any business endeavor. But given the current economic climate brought on by the COVID-19 pandemic, along with the perennial need for businesses to streamline costs in order to divert savings into the core business, many are asking: Is there a better, more cost-efficient way of implementing a database solution without sacrificing enterprise-ness?

The answer is most certainly! Let me introduce you to PostgrEX.

What is PostgrEX?

PostgrEX is shorthand for Postgres EXIST Enterprise Xpertise.

It is an enterprise-grade database platform built on top of a purely open-source technology stack and is part of EXIST Software Labs Inc.’s Data Solutions.

What are the components of PostgrEX?

1.    Scoping and sizing of DB hardware

We will recommend the hardware specifications (memory, CPU, storage, networking, etc.) that will be optimal for your business requirements based on the current and projected data growth, the total number of users, total concurrent users, largest table size, largest query size, etc.

2.     Installation

We will install the database system, along with the high availability/failover, monitoring, backup/recovery, and connection pooling components.

3.    Optimization

We will optimize the database configuration settings for the best possible performance given the hardware available.

4.    High Availability/Failover/Disaster Recovery

We will set up replication between the Postgres database servers (streaming replication, WAL log-shipping, or a combination of both) in the Main site and we can also set up replication to a DR site.

We will also set up and configure Patroni, etcd, and HAProxy as part of the failover mechanism of the system.

5.    Monitoring

We will install, set up, and configure pgCluu as the default DB cluster monitoring tool.

6.    Backup and Recovery

We will install, set up, and configure Barman as the default DB backup and recovery management tool.

7.    Connection Pooling

We will install, set up, and configure pgBouncer as the default DB connection pooling tool.

8.    Query Optimization

We can also provide query optimization services to your Developers in order to ensure tip-top application performance.

9.    Migration to Postgres

We can migrate your existing SQL Server, MySQL, and Oracle databases to Postgres CE.

What are the technologies used by PostgrEX?

1.    Database

Postgres, or PostgreSQL, is arguably the best open-source object-relational database management system available today. It was DB-Engine’s “DB-of-the-Year” for 2 years straight (2017 and 2018), and has proven itself in mission-critical applications across all industry verticals.

See: Why use PostgreSQL for your Business?

2.    High Availability and Failover

Patroni – an open-source Python application that handles Postgres configuration and is ideal for HA applications. See Patroni documentation.

etcd – a fault-tolerant, distributed key-value store that is used to store the state of the Postgres cluster. See etcd documentation.

HAProxy – provides a single endpoint to which you can connect the application. It forwards the connection to whichever node is currently the master. It does this using a REST endpoint provided by Patroni. Patroni ensures that, at any given time, only the master Postgres node will appear as online, forcing HAProxy to connect to the correct node. See HAProxy documentation.

3.     Monitoring

pgCluu – a lightweight, open-source Postgres monitoring and auditing tool. See pgCluu documentation.

4.    Backup and Recovery

Barman – an open-source backup and recovery management tool. See Barman documentation.

5.    Connection Pooling

pgBouncer – a lightweight, open-source connection pooler for Postgres. See pgBouncer documentation.

Moving Forward with PostgrEX

Is your organization ready to face the challenges of an uncertain future? Having enough money in the bank is certainly a top priority and doing away with unnecessary and exorbitantly-priced database license costs is one way of doing this.

With PostgrEX, your business applications can still enjoy industry-recognized, top-level, enterprise database excellence through the use of expertly-configured, purely open-source technologies. This means you get to keep your kidney to live and fight another day—and many other days!

Contact us for more information.

Download our datasheet now!