Big Data and Analytics

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.

To learn more about our Data Solutions Services, click here.

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/

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

Exist Software Labs Inc and Informatica Pocket Session: 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.

Jon Teo, Data Governance and Privacy Expert at APJ spoke at the event about the benefits of Data Governance. He demonstrates how Data Governance 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 opportunity 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 data governance approach.

Kingsley Dsouza, a Technical Data Governance Privacy Domain Expert, was one of the speakers who also demonstrated Informatica’s Data Governance 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 data 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.

Download our FREE DATASHEET!

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

Did you miss the event?

Watch the Realizing Data Governance Benefits in a Cloud-Hybrid World Video On Demand now!

IDMC

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

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.

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. And because it is built on top of an enterprise cloud platform, it 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!

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The Metallica of Master Data Management: TIBCO EBX

The Metallica of Master Data Management: TIBCO EBX 768 487 Exist Software Labs

In the world of heavy metal, Metallica is considered, arguably, as the G.O.A.T. Some may contest this claim and cite the forefathers, like Black Sabbath or Led Zeppelin, but the prevailing sentiment is that the ‘Tallica boys are at the top of the heap.

One of the key achievements of this band is that they put out the highest-grossing metal album of all time. Released in 1986, Master of Puppets is Metallica’s best-selling album (surpassing every other metal band in the world in terms of raw sales).

If Master of Puppets is Metallica’s magnum opus, then Master Data Management’s masterpiece is no other than TIBCO EBX.

But first…what is Master Data Management?

Master data management (MDM) is the initiative of an enterprise that is keen on having data work for them to create a single repository of all master data, reference data, and metadata in order to minimize, if not totally eliminate, data errors and redundancy in business processes.

An MDM solution would typically be an interplay of Data Quality, Data Integration, and Data Governance practices.

What’s in it for me with Master Data Management?

The provision of a single point of reference for business-critical information eliminates the costliness of data redundancies that occur when organizations rely on multiple versions of data that reside in departmental silos.

For example, MDM can ensure that when customer information changes, the Sales & Marketing Department will not reach out to unreachable or different entities, but will consistently have a single, latest, and accurate view of the customer upon which to target their efforts.

What are the Basic Steps to Master Data Management?

  1. Discover the relevant and pertinent data sources to be mastered in your enterprise.
  2. Acquire the data (Data Integration proper, ETL, streaming, etc.).
  3. Cleanse the data (Data Quality proper).
  4. Enrich the data with data from other data sources that are external to your enterprise but are useful (e.g. social media, websites, etc.).
  5. Match the data with other data and look/flag for duplication.
  6. Merge the data and select the most up-to-date version of the data.
  7. Relate the mastered data with other relatable data in the enterprise.
  8. Secure the mastered data (masking, user roles & privileges, etc.).
  9. Deliver the mastered data to the appropriate and intended consumers and stakeholders.
  10. Govern the mastered data and ensure that master data management becomes a secure, repeatable, sustainable, and value-generating key framework in the enterprise.

Why rock with TIBCO EBX?

First, a history lesson. TIBCO EBX was the result of the acquisition of Orchestra Networks, a leader in MDM, by TIBCO Software last 2018. This assimilation proved monumental as evidenced by TIBCO EBX’s rankings in Gartner’s evaluations:

As you can see, TIBCO EBX is among the Top 2 leaders in the Leader quadrant, alongside the very expensive Informatica.

In actual MDM use cases, however, TIBCO EBX ranked highest in 5 of 6:

The latest 2020 Gartner report on the MDM space pretty much tells the same story:

tibcomdmgartner2020

Again…why rock with TIBCO EBX?

ONE PLATFORM FOR ALL YOUR DATA MANAGEMENT NEEDS

With EBX software, you only need one platform to do the job of multiple products, including MDM, reference data management, product master data management, party master data management, data governance, and hierarchy management.

SUPPORT FOR ALL TYPES OF BUSINESS FUNCTIONS

Operational and analytical processes may be different, but they have one thing in common: data powers them all. Instead of managing these assets in multiple, separate applications, the EBX platform provides a single resource to govern and manage them, providing consistency and cohesion to processes across your organization.

SUPPORT FOR ALL LEVELS OF USERS

  • Business Users: Delivers an intuitive, self-service experience for your business teams. Users view, search, author, edit, and approve changes in a workflow-driven, collaborative interface.
  • Data Stewards: Helps data stewards easily discern the quality of their data and take action using powerful data governance, matching, profiling, cleansing, workflow monitoring, quality analytics, and audit trail capabilities.
  • Developers/Analysts: Supports building and adapting applications quickly, without long and costly development projects. Project teams have full control over data models, workflow models, business rules, UI configuration, and data services.

FLEXIBILITY AND AGILITY

Custom applications and purpose-built MDM solutions are hard to change, but EBX software is flexible and agile. It uses a unique what-you-model-is-what-you-get design approach, with fully configurable applications generated on-the-fly. Long, costly development projects are eliminated. And EBX software includes all the enterprise class capabilities you need to create data management applications including user interfaces for authoring and data stewardship, workflow, hierarchy management, and data integration tools.1

Is that all?

TIBCO EBX’s best-of-breed capabilities include:

DATA MODELING

What you model is what you get. The flexible data model supports any master domain and relationships as well as complex and simple forms of data.

COLLABORATIVE WORKFLOW

Collaborate with everyone who touches your data. Manage updates, oversee change requests, and provide approvals through a customizable workflow.

HIERARCHY MANAGEMENT

Support any type of hierarchy and create alternate hierarchies without duplication. Now it’s easy to visualize and maintain complex relationships.

VERSION CONTROL

Manage and connect every version of data—past, present, and future.

PLATFORM COMPATIBILITY

Integrate with multiple platforms on-premises or in the cloud. Works with a wide range of interfaces, application servers, databases, and infrastructures.

INSIGHT WITH DASHBOARDS AND KPIS

Track, analyze, and measure data quality and performance through EBX dashboards.2

How can I buy tickets to the next concert?

If you want to learn more about MDM and how TIBCO EBX can help your organization eliminate bad data, data silos, and poor data visibility, contact EXIST Software Labs today!

Keep rockin’!

 

 

 

Footnotes:
1 TIBCO EBX Datasheet
2 ibid.

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Why is Greenplum the Best Choice for a Cloud Data Warehouse?

Why is Greenplum the Best Choice for a Cloud Data Warehouse? 768 487 Exist Software Labs


The Best MADP

Data is the drivetrain of digital transformation and the enterprise with the ability to tap into all possible data sources in order to gain actionable insights is at a key advantage.

In order to gain this advantage, a Modern Analytics Data Platform (MADP) is required. What are the attributes of an MADP that make it the technological foundation of digital transformation?

12attrsofMADP

Greenplum ranks high in every one of these attributes, ensuring the enterprise of continuous access to valuable insights.

In fact, Gartner has ranked Greenplum as the No. 1 open source Data Warehouse platform for 2019, with only the very costly Teradata and Oracle above it:

greenplumgartnerno1 1

This combination of being a premier MADP and no-comparison cost-effectiveness makes Greenplum the leading choice for most enterprises seeking data-driven digital transformation.


Moving the Data Warehouse to the Cloud

There are many benefits to moving your enterprise data warehouse to the cloud aside from the more common advantages of mitigating the cost of and simplifying management, administration, and tuning activities.

The following are some of the more salient benefits:

1. Vertical and horizontal scalability – With the influx of ever-increasing volumes and varieties of data come the need to be able to add processing and storage capability to your existing data warehouse infrastructure in a quick and agile manner. This also includes the ability to scale out and add more nodes as the number of users increases.

2. Drastically-reduced start-up and operating costs – The risk of investing millions of dollars in on-premise machines or appliances only to have them become outdated in a number of years is eliminated with the cloud’s pay-only-for-what-you-use-when-you-use-it model.

3. Agile feature enhancement – Advances in data analytics call for products that are quick to adapt to these new features. The cloud infrastructure allows for seamless integration of new functionalities behind the scenes.

4. Top-notch support – Access to 24/7 support by a team of experts means that your system never has to go down, allowing for stellar SLA fulfillments.

5. Security – Since the top cloud providers are required to meet strict security standards set by health, financial, and government entities, you can be assured that your data is kept safe, making it easier to attain certifications like ISO27001, SOC2, GDPR, HIPAA, and PCI. Authorization, authentication, logging, and auditing are basic to all these platforms.


Greenplum in the Cloud 

Pivotal Greenplum is available in the 3 major cloud service providers: AWS, Azure, and GCP. 

Greenplum on AWS

  • Same Pivotal Greenplum software as on-premises or cloud installation
  • Secure Deployment with Product Review from Amazon
  • GP Browser included (Web based SQL Query Tool)
  • Optional Installer makes installing additional components such as MADlib and Command Center easy!
  • Self Healing automates node recovery without administrative intervention
  • Snapshot Utility automates instant and non-blocking database backups
  • Optimized Deployment for Performance using Best Practices
  • Development to Production Deployments via AWS Cloud Formation
  • PgBouncer Connection Pooler included and preconfigured
  • Upgrade Utility notifies and automates cluster upgrades
  • Disaster Recovery via copied Snapshots simplifies and reduces cost for a DR solution

Greenplum on Azure

  • Same Pivotal Greenplum software as on-premises or cloud installation
  • Secure Deployment with Product Review from Microsoft
  • GP Browser included (Web based SQL Query Tool)
  • Optional Installer makes installing additional components such as MADlib and Command Center easy!
  • Self Healing automates node recovery without administrative intervention
  • Optimized Deployment for Performance using Best Practices
  • Development to Production Deployments via Azure Resource Manager Deployment
  • PgBouncer Connection Pooler included and preconfigured
  • Upgrade Utility notifies and automates cluster upgrades
  • Snapshot Utility automates instant and non-blocking database backups

Greenplum on GCP

  • Same Pivotal Greenplum software as on-premises or cloud installation
  • Secure Deployment with Product Review from Google
  • GP Browser included (Web based SQL Query Tool)
  • Optional Installer makes installing additional components such as MADlib and Command Center easy!
  • Self Healing automates node recovery without administrative intervention
  • Optimized Deployment for Performance using Best Practices
  • Development to Production Deployments via Google Deployment Manager
  • PgBouncer Connection Pooler included and preconfigured
  • Upgrade Utility notifies and automates cluster upgrades

For a more detailed presentation on Greenplum on AWS, watch this:

https://tanzu.vmware.com/content/webinars/apr-2-the-enterprise-data-science-warehouse-greenplum-on-aws

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Why the Data Warehouse Is Here to Stay

Why the Data Warehouse Is Here to Stay 768 487 Exist Software Labs

The buzzword has been “digital transformation” and the phrase continues to announce the importance of leveraging new technology as the catalyst of improvement in the enterprise. New ways of doing things have been introduced and this is no less apparent in how data is now collected and used for business intelligence and analytics.

The advent of Big Data many years ago brought about huge excitement in these areas. The recognition that there is more data to be collected and used in the enterprise saw the emergence of technologies that facilitated the ingestion of all types of data, their storage in distributed file systems, the ability to scale out easily to accommodate more data, and the various means of getting at this data. But there was a problem.

While the ability to capture and store all types of data, including unstructured data, seemed to be the panacea, it became immediately apparent that:

  • Most business data is structured   
  • Everybody knows SQL
  • The relational model is popular
  • Dimensional modeling works

While it is true that the Big Data “data lake” has the potential of opening up more insights due to the volume and variety of data, real-world use cases have shown that actionable data almost always came in the form of SQL-interfaced, relational data. And this is why the Data Warehouse never really went away.

But the modern data warehouse is a vastly different animal than the traditional data warehouse of years gone by. For a data warehousing platform to be called modern and a true agent of digital transformation, it must have the following attributes:

  • Support any data locality (local disk, Hadoop, private and public cloud data.)
  • In-database advanced analytics.
  • Ability to handle native data types such as spatial, time-series and/or text.
  • Ability to run new analytical workloads including machine learning, geospatial, graph and text analytics.
  • Deployment agnostic including on-premises, private and public cloud.
  • Query optimization for big data.
  • Complex query formation.
  • Massively parallel processing based on the model, not just sharding.
  • Workload management.
  • Load balancing.
  • Scaling to thousands of simultaneous queries.
  • Full ANSI SQL and beyond.
  • MPP data warehouse able to run seamlessly on-premises, public or private clouds, with a much-expanded mission from previous designs.
  • Primarily based on open source projects with strong communities behind them.
  • Supporting both data science computation and preservation and publishing of data science models.
  • In-database analytics and data science libraries. The alternative is running machine learning algorithms against Hadoop or cloud repositories, but needing to move results to another platform for further analysis and presentation (visualization, dimensional models for scenario planning, etc.)
  • Able to support cost-based query optimizations on polymorphic data, while delaying analysis of the data structure until runtime. 1

As you can see, a Hadoop Big Data implementation and the modern Data Warehouse, combined, can become the all-encompassing data platform and single source of truth of an enterprise.

With that said, the best open source-based, modern data warehousing platform in the digital landscape today is Pivotal Greenplum.

pivotalgreenplumarchi

In a succeeding blog post, we will discuss the many features that make Pivotal Greenplum the best data platform for data-driven digital transformation.

 

Notes:
1  Neil Raden, The Data Warehouse in the Age of Digital Transformation

 

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Software Innovation CEO to Stage Data Warehousing, Big Data on IT for Renewable Energy Tech Forum

Software Innovation CEO to Stage Data Warehousing, Big Data on IT for Renewable Energy Tech Forum 768 487 Exist Software Labs

Having paved the way for digital transformation in the local power and energy sector, EXIST Software Labs, Inc. continues its message of business profitability through information technology by being one of the key participants in the “Information Technology for Renewable Energy (IT for RE) Technical Forum”. The event is organized by the Department of Energy (DOE) in partnership with the United Nations Development Programme (UNDP), to be held on September 12, 2019, at The Legend Villas, Mandaluyong City.

Mr. Mike Lim, CEO and president of EXIST, will be speaking on the topic of “Data Warehousing VS. Big Data”. The talk will shed light on the importance of business intelligence and how these two enabling technologies could catalyze further innovation in the nation’s power and energy sector.

“The presentation is about data warehousing and big data: how they are different from each other, when to use one over the other, and when both can be used in certain situations. The audience can expect to know what data warehousing is, what big data is, and how it is used in real-world scenarios,” Mr. Lim explains.

According to the CEO, the audience will be given the tools and categories to better appreciate the data that they have and decide which of the two approaches, or even combined, would best suit their needs. A discussion on how other power and energy organizations have adopted these technologies would then fill out the picture.

“They will learn when to transition to a Data Warehouse or when to transition to a Big Data stack depending on certain situations that can be driven either by business or by the amount of data that they have. The audience will be able to decide, especially in the renewable energy sector, if they will now be more open to applying big data to their organization. I will also present some real-world use cases in both our region and other regions so they would have more appreciation of the technology.”

As a veteran of the digital transformation speaking circuit, Mr. Lim expressed gratitude for being given the opportunity to represent EXIST Software Labs, Inc. in this event. 

“As a software company, it is a privilege to be able to share our thoughts, our knowledge, our experience to organizations like DOE. The bottom line is how we as a company can provide real business value to DOE concerning technology and how this translates to more clear, real-time and faster decision making. Being able to be part of that process is a highlight in our organization. As I said, it is a privilege. It means that we are living up to our purpose as a software technology company by contributing to the nation’s progress through the advancement of appropriate technologies. It is a big milestone for us,” he asserts upon concluding the interview.

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Enterprise Technology Solutions Leader, Exist Reveals Top 5 IT Trends for 2019

Enterprise Technology Solutions Leader, Exist Reveals Top 5 IT Trends for 2019 768 487 Exist Software Labs

The demand for business companies to go digital continues as they face the new year with new expectations, competitors, channels, threats, and opportunities. Digitalization has created a new breed of market that companies of all sizes- small, medium-sized or even large corporations cannot ignore. Traditional businesses have now accepted using digital transformation as a business strategy— to deliver products and services through web, reap data from every market interaction then gain insights to rapidly optimize their value chain and help them increase competitive advantage.

By transforming digitally, businesses are able to build a connection with their customers, speed up the pace of innovation and, as a result, claim a greater share of profit. Today, companies that invest in digital transformation are building an edge over those who don’t, that will enable them to succeed in reaching the expanding digital lives of consumers that encompass the rules of engagement that strongly influence customer loyalty.

With this profound effect on business organizations, allow us to share what are the IT Trends our top executives think would make an impact by the year 2019:

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Blockchain

By allowing digital information to be distributed but with a highly secured transaction, blockchain technology created the backbone of the new type of Internet. What do we really mean when we say blockchain? According to Don and Alex Tapscott, authors of the book Blockchain Revolution (2016), “the blockchain is an incorruptible digital ledger of economic transactions that can be programmed to record not just financial transactions but virtually everything of value”. The growing list of records found in blockchain is called blocks which are linked using cryptography.

In an interview with Mr. Mike Lim, President & CEO of Exist, he stated, “blockchain has been really gaining quick traction not only because of bitcoin or cryptocurrency but because of the promise of more transparent but secured communication between B2B companies or even B2C depending on what vertical you are.” Blockchain gives internet users the ability to create value and authenticates digital information. By storing data across its peer-to-peer network, blockchain eliminates a number of risks that come with data being held centrally. Every network participant validates the transaction so that the data stored is immutable and cannot be forged.

Real-world applications of blockchain technology are becoming more mainstream resulting in the amount of transactional data to become huge. Combining blockchain and big data sparks a new level of analytics. Executives believe that the blockchain promise of secure, traceable transactions and improved transparency of information can streamline supply chain management. Thus, continuing to make a disruptive change in technology by the year 2019.

 

Big Data

With today’s digital technologies, it’s possible to analyze your data and turn it to insights rapidly, enabling enterprises to make better decisions.

According to Gartner, big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation. As the head of the healthcare services at Exist and the VP for Sales and Marketing, Mr. Willex Perez shared to us his thoughts: “Imagine the possibilities of what big data can do for predicting illnesses. If you collect enough clinical information you’ll be able to compare your status or your clinical values with others. After which, you can search available research or studies to check your risk rating as an individual.”

Emphasizing on healthcare, Mr. Willex added that among the growth trends the use of big data in healthcare will be essential. “With analytics, enterprises will be able to drive innovation and come up with intelligent business decisions. While organizations collect data for analytics purposes combined with IoT as another major source for data,” Mr. Willex concludes, “it is inevitable to use big data analytics to complete the picture.”

 

Internet of Things (IoT)

Countless business opportunities are in the fire hose of IoT data as products and services have become more connected. Internet of things refers to the network of devices such as home appliances, mechanical and digital devices that contain electronics, software, and connectivity which allows them to interact and exchange data.

Considered as one of his top IT trends, Mr. Christopher Silerio, the VP for Operations at Exist, believes IoT sensors provide us a valuable real-time update of the exchange of data with the sources. He shares, “there will be a time when there will be more data exchanges happening between sensors even without human interaction.  From smart appliances or smart meters, devices will continuously send data signals to a certain component or machine, providing information in real time. It’s reasonable to say that IoT has begun to transform the business landscape and is expected to continue in 2019.”

 

Cloud

While IoT generates huge amounts of data, the cloud ensures that these are captured and stored properly. The simplicity and accessibility of cloud computing to manage vast amounts of data remain a catalyst enabling the rapid expansion of IoT. Cloud computing provides small to medium enterprises the ability to enjoy low implementation cost for their total IT infrastructure and software systems.

Utilizing the abilities of cloud computing, enterprises of all sizes can deploy applications a lot quicker and cheaper compared to the cost of setting up whole IT Infrastructure and service by themselves. According to Forrester’s predictions for 2018, the total global public cloud market will be $178B in 2018, up from $146B in 2017, and will continue to grow at a 22% compound annual growth rate. From this perspective, the cloud seems to be a key driver of digital transformation and economic growth.

“Cloud makes it easier for organizations to worry more about their business process rather than infrastructures. It makes it easier for startups to build their business quickly,” reveals Mr. Jonas Lim, the VP for Technical Services at Exist. “As early as almost a decade ago, we believed that cloud computing is a real game-changer and it has proven to be true as the future continues to bring us into a world of unlimited connectivity empowered by the cloud,” he further adds.

 

Artificial Intelligence

Artificial intelligence or AI doesn’t only apply to robotics. As a branch of computer science, AI involves the development of computer programs to complete tasks which would otherwise require human intelligence. As evidence of its spread, AI is even available for use along with other cloud solutions by which businesses can just subscribe to.

Internet technology companies also make use of  AI to optimize their IT infrastructures. In fact, according to Wikibon: “AI-optimized application infrastructure is one of today’s hottest trends in the IT business. More vendors are introducing IT platforms that accelerate and automate AI workloads through pre-built combinations of storage, compute, and interconnect resources.”

Mr. Jonas Lim pointed out the increasing use of chatbots in business services. Chatbots are programs built to automatically engage with received messages simulating actual human interaction. In addition, artificial intelligence might just be ready to explode with its use, particularly inside the healthcare industry.

What started with manufacturing has now spread to knock and open the doors to greater digital business scale but now with analytics and computing intelligence at the forefront of cutting-edge changes in the upcoming years. “[Like] growing population of robotics is bound to happen,” Mr. Willex added, “and although we don’t know the future, it is quite evident that interacting with AI will soon be part of our everyday lives.”

web 800x507 Dont Get Wiped Out Riding the Big Data Wave Hang Ten with Informatica Big Data Management 768x487 1

Don’t Get Wiped Out Riding the Big Data Wave: Hang Ten with Informatica Big Data Management

Don’t Get Wiped Out Riding the Big Data Wave: Hang Ten with Informatica Big Data Management 768 487 Exist Software Labs

Data has always been the key factor in business computing. However, the role that it plays has evolved throughout the years. These evolutionary epochs have generally been termed as the 3 waves of data management.

Wave 1: The Rise of Relational

In the first wave, we see the emergence of the relational model and relational database management systems as an improvement upon the flat file data store. Having the advantage of a structured query language (SQL) to extract data from the database enabled businesses to more easily derive value from their data.

Data in this era was used to support specific business processes and applications.

Data served the application.

Wave 2: Eyeing the Enterprise

The second wave will have data being used in a more enterprise-wide fashion. Here we see the emergence of the use of unstructured data in the form of documents, web content, images, audio, and video in Enterprise Content Management (ERM) systems. Other applications would be Enterprise Resource Planning (ERP), supply chain, etc.

Data served the enterprise.

Wave 3: The Tsunami of Data

We are currently in the 3rd wave. Vast improvements in cost efficiencies in the areas of storage, network speed/reliability, memory, and over-all computing capability have paved the way for the emergence of Big Data.

Simply put, Big Data is the ability to gather very large amounts of all kinds of available data (structured, semi-structured, unstructured) at various latencies (even real-time), profile the data, catalog the data, and parse/prepare the data for analysis, all done in a distributed file and processing architecture.

Data in the 3rd wave is front and center. It now transforms business processes (see Wave 1) and creates new business models (see Wave 2).

Data powers digital transformation.

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Wipeout Points with Big Data

The following are some pain (wipeout) points with Big Data:

1. Functionality and performance gaps of processing engines on Hadoop – These frameworks (such as MapReduce, Hive on Tez, and Spark) are good for certain use cases but lack the core functional and performance requirements for big data integration.

2. Provide faster and flexible development – a big data journey should be lean and agile, focusing on automation, reusability, and data flow optimization.

3. Search data assets in Hadoop and the Enterprise – a solution that enables easy searching and discovery of relevant data sets is not readily available. There is the need to answer the question: How do I find my data and know their relationships?

 

Ride the Wave with Informatica

It must be noted that Informatica has been the leader in data management in Wave 1 and Wave 2.

With Wave 1, Informatica pioneered and defined ETL and data integration categories. They are still the market leader in these areas.

With Wave 2, as data became enterprise-wide, Informatica added data quality, master data management, cloud integration, data masking, and data archiving to their solution portfolio. They are the market leader in each of these categories.

Hanging Ten with Informatica Big Data Management

Hadoop Ecosystem

With the arrival of YARN, the capability to build custom application frameworks on top of Hadoop to support multiple processing models was realized. What Informatica Big Data Management (BDM) did was combine the best of open source (i.e., YARN) and 23 years of data management experience to build out Informatica Blaze.

So what is Blaze? You can look at Blaze as a cluster-aware, data integration engine for Hadoop—built using in-memory algorithms, all in C++—for Big Data batch processing. It’s integrated with YARN, so you can expect it to be a very scalable and very fast, high-performance distributed processing engine for Hadoop.

But does Blaze replace the other Big Data processing engine frameworks? Does it replace MapReduce, Tez, or Spark? The answer is No. What Blaze does is actually complement the capabilities of the other processing engines by virtue of the fact that there is not one solution to solve all of the Big Data batch processing use cases.

What Informatica did to overcome the functional gaps of the other processing engines was expose their transformation libraries (built over 23 years) to the Hadoop ecosystem—to a distributed processing platform—through the Informatica Blaze engine. What that allowed Informatica to do was open the floodgates to a lot of their functionality (not just the core functionality of joiner transformations, aggregates, and look-ups, but also their complex data integration transformations: the complex data quality, data profiling, and data masking transformations) through the Blaze engine, making it much easier for you to implement complex ETL processing in a Hadoop ecosystem. In terms of performance, what Informatica did was they took Blaze and made it an in-memory processing engine built purely on C++.

If I execute a mapping on the Hadoop cluster, you may be wondering, will it automatically default to the Blaze engine? Not necessarily. Informatica BDM has this key innovation for the Hadoop ecosystem called the Smart Executor. It’s a polyglot engine. This means that it has the ability to understand multiple languages and implies that not one technology will solve all the Big Data integration use cases. What it does is it automatically, dynamically, and intelligently selects the best execution engine to process the data based on various parameters like mapping, workload type, and infrastructure configuration. It will optimize that mapping and, based on the cluster configuration, determine which is the best execution engine to run it on and could pick either of the engines as faster than the others. It is built to intelligently pick the best execution engine.

Informatica Blaze

As the graph above indicates, Informatica Blaze is faster than Spark and Hive on MapReduce. But why?

With its multi-tenant architecture, Blaze allows you to run concurrent jobs served by one single Blaze instance. This translates to optimized resource utilization and sharing amongst jobs. So even if you have a thousand mappings for execution, Blaze will only launch one YARN application to serve this requirement. Also, as mentioned earlier, Blaze was written in C++ code, providing better memory management compared to a Java-written framework.

Blaze also uses the Data Exchange Framework (DEF), a process for the shuffle phase, which is an in-memory built framework that shuffles data amongst the nodes without the loss of recovery—a very key capability in Big Data processing for Big Data processing engines.

 

Safely Back to Shore

What your business does with data will determine whether it will wipe out and sink to the bottom or ride the wave all the way back to shore.

With Informatica and Informatica Big Data Management, you can be assured that your data will be made to drive the digital transformation needed to ensure that your business is empowered and not floundering around.

 

 

 

References:
1. Module 04: Informatica BLAZE Overview: Big Data 10.x: Black Belt Enablement (Module) (internal partner resource)

2. Keynote: CEO Anil Chakravarthy – Informatica World 2016 )

3. Big Data for Dummies by Judith Hurwitz, Alan Nugent, Dr. Fern Halper, and Marcia Kaufman (Hoboken, NJ: John Wiley & Sons, Inc, 2013) (https://www.amazon.com/Big-Data-Dummies-Judith-Hurwitz/dp/1118504224)