Ensure safe operation and constant availability of your application. 

Obtain the highest performance from your critical data technology investments with expert's support.




Liberate your resources from activities that don’t directly contribute to the bottom line while still obtaining the highest performance from your critical data technology investments. Improve your efficiency and reliability with our support team 24x7 coverage. Optimize your resources and achieve cost savings followed by higher business results.

To set the parameters right, a proper process is needed that enables high-quality, fast and innovative solutions delivering the best results.

Outsource the analytics technology and insource an analytics mindset to gain the factors for faster and more effective solutions.

man sitting in front of laptop during meeting

medium_data & analytics.jpg

small_data & analytics.jpg

thumbnail_data & analytics.jpg

data & analytics.jpg

Data & Analytics

Liberate your resources from activities that don’t directly contribute to the bottom line while still obtaining the highest performance of your application.

medium_800.jpg

small_800.jpg

thumbnail_800.jpg

800.jpg

App Design & Development

Add value to your business by utilizing machines and advanced analytics to find new ways of optimization and profit.

medium_ai & machine learning.jpg

small_ai & machine learning.jpg

thumbnail_ai & machine learning.jpg

ai & machine learning.jpg

AI & Machine Learning

Drive Your Business Forward!

Sales

iOLAP Experts are Here to Assist You.

experts

Sales CTA

medium_irobot-b.jpg

small_irobot-b.jpg

thumbnail_irobot-b.jpg

irobot-b.jpg

## Client Description
iRobot is a global consumer robot company that designs and builds thoughtful robots and intelligent home innovations that make life better. iRobot introduced the first Roomba robot vacuum in 2002. Today, iRobot is a global enterprise that has sold millions of robots worldwide. iRobot’s product portfolio features technologies and advanced concepts in cleaning, mapping and navigation. Working from this portfolio, iRobot engineers are building robots and smart home devices to help consumers make their homes easier to maintain and healthier places to live.

## Challenge
iRobot is focused towards building a next generation data platform. Millions of robots across the globe generate enormous amounts of telemetry and sensor data needed across multiple departments to provide better customer service, engineer better robots, and identify new products and services.  Additional data is needed to understand mobile application interactions, the manufacturing pipeline, and customer service incidents.  Due to the volume, variety, and velocity of these different sources, a well architected data platform was needed that ensured security, stability, and scalability.  Moreover, with global data sovereignty and PII regulations, iRobot needed a solution to protect and segregate data assets appropriately.  

## Solution
iOLAP inherited an architecture that was not able to scale and was constantly failing on basic data ingestion.  iOLAP quickly took an inventory of assets, designed a roadmap, and began implementing the next generation framework.  We opted to utilize more AWS managed services which by nature are more fault tolerant and scalable.  Spark jobs were replaced with Lambda, Firehose, and Data Pipeline.  An expensive NoSQL database was replaced by ElastiCache and Athena.  Solutions were introduced to eliminate data loss further upstream in the ingestion.  In Redshift, we implemented proper dimensional models with appropriate compression, sorting, and distribution.  
## Benefits
iRobot’s data platform can now support any workload regardless of volume, velocity, and variety of data.  Infrastructure costs are significantly lower and business users have more trust in their data.  iRobot is now exploring ways to leverage these data assets outside the scope of cleaning floors.
  
![center-small](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/i_Robot_Logo_Green_CMYK_Print_2503d36b83.jpg)



scan_iolap_footer_logo.png

Frisco, Texas

scan_geopoint_footer.png

Austin, Texas

Rijeka, Croatia

Default Footer

iolap-logomark-hob-reversed@2x.png

Homepage top menu

Capture & process streaming data from more than twenty (20) million devices globally for advanced analytics.

Default Heading Title

large_casestudiesDarkBlue.jpg

medium_casestudiesDarkBlue.jpg

small_casestudiesDarkBlue.jpg

thumbnail_casestudiesDarkBlue.jpg

casestudiesDarkBlue.jpg

iRobot

medium_nuvei.jpg

small_nuvei.jpg

thumbnail_nuvei.jpg

nuvei.jpg

## Client Description  

Nuvei Technologies Corp. (formerly Pivotal Payments) is a provider of payment processing services that deliver safer and smarter transactions. Integrated POS and mobile payments offer businesses efficiency and profitability across all sales channels and platforms with a focus on security and continuous investment in research, product development and innovation.

## Challenge  

Previously a single source of truth did not exist for any of their data warehouse dimensions.  Many of the business users used their own version of the supplied reports based on dimensions from local sources that they deemed pertinent creating inconsistency in the monthly numbers.

## Solution  

iOLAP worked with the team to design an MDM solution that looked at all of the available sources and leveraged the most important sources in order based on business rules.

## Benefits  

The IT team benefited from the implementation of the MDM system by dramatically reducing the number of inquiries from business users in regards to changing numbers from month to month.  The business now has a true baseline and can rest easy that the information that they view can be used with confidence.
  
![center-small](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/Nuvei_Logo_1fbac23203.jpg)



One unified and trustful data source for the whole organization that enables better management and faster response rates.

Nuvei

medium_capitalogix.jpg

small_capitalogix.jpg

thumbnail_capitalogix.jpg

capitalogix.jpg

## Client Description
Capitalogix develops “Hedge Fund in a Box” technology with thousands of Artificial Intelligence Systems evaluating global markets utilizing High Performance processors and Massive Parallel Processing.  Capitalogix finds trades that are working, when it is working, in real-time to generate trades with a competitive edge.

## Challenge
Capitalogix trading platform needed real-time database response to execute trades for commodity futures.  iOLAP architected and designed an IBM Netezza MPP architecture to replace a non-responsive legacy system.

## Solution
Our team utilized IBM InfoSphere DataStage, NZSQL, and the processing power of IBM Netezza, to streamline Capitalogix data feed and consumption to support their real-time data requirements.

## Benefits
Capitalogix ROI was immediate with the sun setting of their legacy system and gained the ability to streamline their trading platform in real-time.  Capitalogix is now executing in excess of 2 million queries per day with an average response time of < 1 second.
  
![center-small](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/Capitalogix_white_grey_c1d5da634f.jpg)


Executing in excess of 2 million queries per day with an average response time of < 1 second.

Capitalogix Trading

Our team prides itself on broad functional and technical skills. When you work with us you get the benefits of 20+ years of client service and experienced staff.

Deep Expertise

Backed by ITIL industry standards, we develop additional customized processes and automation. By providing frequent reports, we ensure every process and change is covered and approved.

Proactive & Custom Solutions

Our team will partner with you and your organization to help monitor and identify areas for improvement and optimization.

Continuous Optimization

Scalable staffing to support incidents and requests, monitoring & alerting, and nightly data processing to ensure successful stability of your data platform.

24/7 Coverage

Our Guiding Principles and Values

scan_linkedin_footer_logo.png

scan_facebook_footer_logo.png

twitter_footer_logo.png

js.png

Liberate your resources from activities that don’t directly contribute to the bottom line while still obtaining the highest performance from your critical data technology investments. Improve your efficiency and reliability with our support team 24x7 coverage. Optimize your resources and achieve cost savings followed by higher business results.

Managed Services

large_MSDarkBlue2023.webp

medium_MSDarkBlue2023.webp

small_MSDarkBlue2023.webp

thumbnail_MSDarkBlue2023.webp

MSDarkBlue2023.webp

graphical representation of charts and graphs

large_152-Converted-1-scaled.jpg

medium_152-Converted-1-scaled.jpg

small_152-Converted-1-scaled.jpg

thumbnail_152-Converted-1-scaled.jpg

152-Converted-1-scaled.jpg

Prospective Risk Management (PRM) has been a valued Managed Services customer since 2016, and iOLAP has become a trusted partner. Three years ago, iOLAP assisted PRM in cost saving solutions that included a 3-year reserved instance purchase for all of their EC2 instances. Prior to this, we had assisted other clients in purchasing reserved instances and reserved database nodes. These purchases were typically 1 year all upfront purchases.
PRM’s request was a far more aggressive 3 year all upfront reserved instance purchase. This required additional considerations and planning to ensure the cost savings was applied for the full 3-year lifecycle. The steps we took included:
  
## System Bake-in time
  
Extended evaluation of the instance usage was used to determine the process traits of the applications running on the instances. After enough time of consistent running, an in-depth review of the predicted growth was used to size the EC2 instances.
  
## Analyze the best cost savings approach
  
The goal of the reserved instance purchase was to save money but avoid the trap of going as cheap as possible. Blindly picking the lowest cost option was not going to deliver the best results over the 3-year duration. Some decisions made, such as selectively purchasing convertible reserved instances, gave us the cost savings coupled with the ability to adapt the instances as needed.
  
## Review the plan
  
The reserved instance purchase plan went through a series of reviews at iOLAP followed by knowledgeable individuals at PRM and their parent company. AWS consulting was also contacted to evaluate the systems and compare their results with the purchase plan. This helped to ensure agreement on all sides before the final purchase.
  
Once the purchase was made, there were a few months without recurring EC2 spend. However, business demands gradually brought in additional EC2 costs. For example, a core application was reaching the end of its maintenance support and needed to be upgraded. The upgrade lifecycle created additional on-demand EC2s with their related costs for a few months during the testing and rollout. This highlighted one of the shortcomings of the reserved instance because the purchase is for a fixed number of monthly runtime hours for a specific size and type. The additional instances used during application update could not take advantage of the special pricing.
About 6 months prior to the end of the 3-year period, iOLAP and PRM began reviewing the cost and performance of the existing reserved instances and started planning the next cost saving purchase. Part of the discussion included changing from reserved instances to a recently released AWS savings plan program.
  
The savings plan is offered in 2 varieties. One is an EC2-only plan which prepays for the cost of EC2 instances of a particular type. However, unlike the reserved instance, the EC2 savings plan covers all instances sizes with the same type in a single savings plan. This allows flexibility to grow and shrink EC2s as needed without worrying about keeping the reserved instance in sync. The EC2 savings plan will also cover additional EC2 instances as they are needed. The only caveat is there is no convertible type capability as in the reserved instance, so changing an EC2 from a general type to an optimized type will remove its usage from the savings plan.
The second type of savings plan is a compute savings plan and allows the prepayment of compute resources including Lambda functions and Fargate as well as EC2 instances. This can offer expanded savings capability beyond EC2 services to allow savings on many types of serverless processing. Also, the EC2 compute savings plan is not limited to a particular instance type, so changing from a general type to an optimized type will continue to be covered in the savings plan.
  
For PRM, the evaluation and purchase of the savings plan was a little more involved than the reserved instance. AWS provides an estimator that reviews historical usage of the EC2 instances, and if nothing is being changed, the estimator can help deliver an accurate purchase price. However, the instances at PRM needed to be updated and resized, so there was no accurate history for the estimator. In this case, manual cost calculations were needed.
  
To perform the manual cost calculation and purchase, we used the EC2 savings price calculator page at https://aws.amazon.com/savingsplans/pricing/.
  
![center-small](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/3_a8cc441999.png)

  
On this page we chose the table for Compute Savings Plans for Amazon EC2, and set the options on the page. This rate was used to calculate the expected cost per EC2 instance for a side by side comparison against the on-demand and reserved instance costs. For the purchase, we added all the applicable saving plan rates, and that value was used for the savings plan purchase.
One item that must be considered before purchasing a savings plan, all savings plans are enterprise wide. This can be detrimental to departments of large organizations with centralized billing for the entire company. A savings plan purchase tailored for a department has the potential to be used by other EC2s and compute services in any of the company’s other AWS accounts. This can make the advantages of a savings plan unusable when purchased at the department level.
However, iOLAP’s Consolidated Billing Offering can help with this. It can be tailored to help limit the savings plan usage to a select group of AWS accounts to meet the demands of the department. PRM selected to use the iOLAP Consolidated Billing Offering along with a compute savings plan purchase. This gives PRM additional cost savings and additional value through support savings, trusted advisor services, cost optimization reviews, and more.
  
![center-medium](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/4_e82f03cfa7.png)
  


Amazon EC2 Cost Savings at PRM

large_blogDarkBlue.jpg

medium_blogDarkBlue.jpg

small_blogDarkBlue.jpg

thumbnail_blogDarkBlue.jpg

blogDarkBlue.jpg

Prospective Risk Management (PRM) has been a valued Managed Services customer since 2016, and iOLAP has become a trusted partner. 

large_16538.jpg

medium_16538.jpg

small_16538.jpg

thumbnail_16538.jpg

16538.jpg

## Introduction
  
AWS Glacier is a secure, durable, and extremely low-cost Amazon S3 cloud storage class for data archiving and long-term backup. It is designed to deliver 99.999999999% durability and provide comprehensive security and compliance capabilities that can help meet even the most stringent regulatory requirements. Customers can store data for as little as $1 per terabyte per month, a significant savings compared to on-premises solutions.
What you wouldn’t expect from a simple service like long term storage is its surprisingly complex pricing scheme which consists of storage pricing, retrieval pricing, retrieval request pricing, upload request pricing, expedited request pricing and many more that are fully listed here.
Out of all the pricing items, the one which most often catches a customer off guard is the seemingly low cost of the upload request pricing, which will be discussed in the customer use case below.
  
![2.png](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/2_ba07f290a9.png)
  
## Customer issue
  
As with every AWS service, following best practices and reading through the FAQ is always necessary before implementing. Failure to do so will result in suboptimal performance, or slightly to massively increased billing. In this case the customer created a lifecycle policy on a bucket which moves files to IA after 30 days and glacier after 60 days. A completely reasonable approach considering the bucket contains an archive of ingested files and is unlikely to need retrieval after those 60 days.
What they failed to account for is the request pricing and a certain entry in the FAQ:
  
![3.png](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/3_0160c704fb.png)
  
If you ingest 350,000 files daily, and do not aggregate them, at the end of the month in addition to the storage costs you will pay for 10,248,602 requests, and your bill will look like this:
  
![4.png](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/4_267c96e3c6.png)
  
Now let’s consider the math behind the process, and whether the client actually saved any money with the move to glacier. We shall keep it simple and look at storage costs and requests costs. As you can see above, the client’s requests cost is $514.23, so the storage savings need to be at least that to break even. Calculating the difference per GB for IA storage and glacier we come up to the following:
  
- Infrequent Access Tier, All Storage / Month $0.0125 per GB
- S3 Glacier, All Storage / Month $0.004 per GB
  
We can see that IA storage is 3,125 times more expensive then Glacier so the breakeven point would be roughly 60TB for which the IA storage cost would be $750 and glacier storage cost $240. If we consider the client actually only stored 1TB of data (file size around 100 KB), we can see that the cost savings of storage is $8 with the request cost being $514.23 totaling a net loss of $506.32 monthly!!!
  
Another example is a $12,000 bill incurred by one of our clients after they moved their entire streaming data history to glacier. The files were on average 4.5KB each and again totaled around 1TB in size. With 240 million requests and the monthly cost savings of $8, it would take the client exactly 125 years for their move to glacier to pay itself off.
  
## The solution
  
The solution, as it is in most cases with AWS, is to follow the best practices and aggregate and compress your files before storing them in Glacier. For the 2nd scenario, the solution is to simply download all the files to an ec2 instance manually, zip them all up, and send them to a glacier vault. Of course, one must not forget that you still must pay for the GET request ($0.0004 per 1000 requests) which would come out to $96, but still significantly cheaper than $12,000.
The first scenario, while slightly more complicated, is still very simple to resolve. In our case we decided to use the spare computing resources of one of our EC2 instances and run a bash script which would run once a week, download the files, compress and archive them, and send them to a glacier vault.
  
```#!/bin/sh```
  
```# This script expects a date as the first argument, format YYYYMMDD e.g. 20200120```
```# the second argument expected is the number of days that will be subtracted from```
```# the provided date to create a date range.```
  
```# EXAMPLE: sh ./s3-to-glacier.sh 20200104 3```
```# The above command will download 20200104, 20200103, 20200102, 20200101```
  
```DATE=$1```
```DAYS=$2```
  
```SOURCE_BUCKET=""```
```VAULT_NAME=""```
  
```for i in $(seq 0 $DAYS)```
```do```
```     CURRENT_DATE=$(date -d "$DATE-$i days" +'%Y%m%d')```
```     BACKUP_END=$(date -d "$DATE -$DAYS days" +'%Y%m%d')```
```     echo "==========Downloading s3://$SOURCE_BUCKET/$CURRENT_DATE/=========="```
```     aws s3 cp --recursive "s3://$SOURCE_BUCKET/$CURRENT_DATE/"``` ```"./backup_${BACKUP_END}_${DATE}/$CURRENT_DATE"```
```done```
  
```zip -r "./backup_${BACKUP_END}_${DATE}.zip" ./backup_${BACKUP_END}_${DATE}```
  
```aws glacier upload-archive --account-id - --vault-name $VAULT_NAME --body``` ```"./backup_${BACKUP_END}_${DATE}.zip"```
  
rm "./backup_${BACKUP_END}_${DATE}.zip"
rm -r "./backup_${BACKUP_END}_${DATE}"
  
#### Conclusion
  
While deceivingly simple, every AWS service and especially its billing needs to be thoroughly examined. Failure to do so will result in unexpected, sometimes devastating billing charges which can be avoided through experience and diligence in research.
These two scenarios demonstrated how a seemingly simple task, and a correct business decision of archiving old and very infrequently used files to glacier, turned into a devastating blow to the billing charges due to a lack of understanding of the pricing model of an AWS service.

AWS Glacier is a secure, durable, and extremely low-cost Amazon S3 cloud storage class for data archiving and long-term backup.

How to avoid hidden AMAZON Glacier charges

large_2859745.jpg

medium_2859745.jpg

small_2859745.jpg

thumbnail_2859745.jpg

2859745.jpg

Microsoft

#### Introduction
  
Data is the most valuable asset of the 21st century. Lately, collecting and analyzing data became not only desired, but mandatory for any business’ success. In order to answer relevant questions and evaluate outcomes, companies often reach out to data analysts and BI specialists who turn data into cognizable information.
The challenge of data rests in the speed of its generation. According to Forbes, every day we produce 2.5 quintillion bytes of data which corresponds to 2.5 million terabytes, and the number grows daily. Although not all data is compatible for analysis, companies still end up with millions of records in a single table within a data warehouse. A large data warehouse will probably be accessed by numerous employees from different departments, from different countries around the globe, for various business intelligence operations. It is crucial to enable those employees ready access while still maintaining data integrity and security.
  
#### Restricting User Access
  
Aside from the physical security of a data warehouse and data encryption, there is another significant method used when securing data warehouses (and information systems in general) – logical access control. The main purpose of logical access control is the protection of resources (hardware and software) from unauthorized remote access.
  
Many vendors of data warehouses include features in their products that simplify design of data warehouse logical access control. In this article, I will introduce you to Row-level Security, a feature that Microsoft has built in their database and data warehouse solutions.
  
#### Row-Level Security
  
As people in Microsoft say, Row-Level Security (or RLS) is designed to help database administrators implement restrictions on data row access. For example, ensuring that employees access only to those data rows that are relevant to their department. Since the restriction logic is located on the database side, rather than in the application that accesses the database, the security system is more reliable and robust.
  
Row-level Security can be applied to SQL Server, Azure SQL Database and Azure SQL Data Warehouse. It supports two types of filter conditions that define row-level access to records in a table, so called security predicates:
  
- Filter predicates – filter the rows that are available to read operations like SELECT, UPDATE and DELETE
- Block predicates – explicitly block write operations that violate the predicate like AFTER INSERT, AFTER UPDATE, BEFORE UPDATE and BEFORE DELETE
  
#### How does it work?
  
![rls.png](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/rls_9ce8245a39.png)
  
In RLS implementation, restricted access to data rows is controlled by a security predicate defined as a function that is invoked and enforced by a security policy. Depending on the security predicate, users can’t access the filtered rows, delete, or update them, and if the block predicate is defined, they are not able to use write operations.
The function that defines security predicate is table-valued function, meaning that it returns data of a table type.
  
#### Use Case Scenario
  
Imagine the following scenario: You work as a database developer for a hospital that provides inpatient and outpatient care. Among the tons of data that needs to be collected, the hospital’s staff wants to keep track of their patients’ records. Every time a patient visits the hospital, they want to record the valuable information such as the date he arrived, diagnosis, treatment, etc. Because of the practice of maintaining the security and confidentiality of patients’ records, you need to ensure that nurses only have access to the patients they care and restrict them access to other patients’ records.
For the sake of this demonstration, let’s keep it simple and say that there are two nurses and one doctor that admit patients on daily basis. Each of them can access the hospital database using their own username, because you have already created three corresponding users in the SQL Server database. Users are Doctor, Nurse1 and Nurse2.
  
![Page-3-Image-3.jpg](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/Page_3_Image_3_1c6dea8ff5.jpg)
  
![Page-3-Image-4.jpg](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/Page_3_Image_4_33223d3890.jpg)
  
You have also created the required table that holds patients’ records and you simply named it PatientRecords. For now, the doctor and two nurses can access all patients’ records since you have granted them select permission. Every patient is assigned to either Nurse1 or Nurse2 which is also documented in the table.
  
![Page-3-Image-5-1.jpg](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/Page_3_Image_5_1_54347bfd9b.jpg)
  
![Page-3-Image-6.jpg](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/Page_3_Image_6_d921eba49f.jpg)
  
Before Microsoft introduced Row-level Security feature in SQL Server 2016, the lowest security level of a database was table-level security, meaning that users could be restricted to access a whole table. The need of row-level restriction was met by writing custom stored procedures or more often by creating views for different users. Those approaches get the work done, but lately it has become difficult to maintain them because of the growing amount of data, and growing number of users that require large numbers of views and stored procedures.
Row-level Security, on the other hand, filters rows based on execution context using security policy, which makes the solution more flexible and dynamic.
As a database developer you like to keep your solution in order, so for easier maintenance you create a new schema that will contain the RLS function with security predicate.
  
![Page-4-Image-7.jpg](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/Page_4_Image_7_dd2f4a9d05.jpg)
  
This function is used to compare incoming @NurseResponsible argument with the username of user that is currently executing the query. The function will return 1 only if the current user is @NurseResponsible or Doctor. Since the same doctor is treating all patients, you need to make sure he has access to all of the patient records.
Next step is to create a security policy that will enforce created function with the security predicate. Since you need to restrict access to rows on a read level, you need to use filter predicate and call the function with security predicate on PatientRecords table. Don’t forget to parse the correct column to @NurseResponsible argument and activate the policy.
  
![Page-4-Image-8.jpg](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/Page_4_Image_8_4df9aec587.jpg)
  
Once you’re done, you want to test your solution, so you execute a simple SELECT statement three times, each time as different user.
  
![Page-4-Image-9.jpg](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/Page_4_Image_9_7d794433a9.jpg)
  
Doctor should see all patients’ records while Nurse1 and Nurse2 should have access to only those patients they are responsible for.
Congratulations! You have just implemented simple but powerful RLS solution.
  
#### Conclusion
  
Data security has become one of the greatest challenges in data-driven world. It is essential to find a compromise between user access and data integrity, security and privacy. This data-people-security balance is often achieved using access control features like RLS.
RLS enables more fine-grained access control execution process of a query. Since you can have multiple security predicates in a single security policy, the security logic is centralized, which benefits the application transparency and there are no changes needed at the application level. However, since RLS is using table-valued functions, it may negatively affect the performance.
Interesting fact: Microsoft also implemented RLS logic in their reporting solution, PowerBI!


Lately, collecting and analyzing data became not only desired, but mandatory for any business’ success.

Data, People and Security – Can They Work Together?

large_Screenshot 2021-04-15 at 15.45.09.png

medium_Screenshot 2021-04-15 at 15.45.09.png

small_Screenshot 2021-04-15 at 15.45.09.png

thumbnail_Screenshot 2021-04-15 at 15.45.09.png

Screenshot 2021-04-15 at 15.45.09.png

iOLAP’s Managed Services Lifecycle

Data is a company's greatest asset, secure it. 

Outsource the analytics technology and insource an analytics mindset to gain the factors for faster and more effective solutions.

Managed Services - Data & Analytics

Keep track and utilize the powerful data. 

Maintain the informational and valued insights to keep your business optimized and on the lookout for new opportunities. 

Managed Services - AI

Focus on business optimization and innovation. 

Liberate your resources from activities that don’t directly contribute to the bottom line while still obtaining the highest performance of your application. 

Application Managed Services - App Dev

We Build On

aws.png

IBM@2x.png

ms.png

We Build On Blue

We Build With

python.png

angular.png

Node.js_logo.png

typescript.png

react.png

docker.png

java.png

Managed Services

iOLAP’s Managed Services Lifecycle

Our Guiding Principles and Values

Deep Expertise

Proactive & Custom Solutions

Continuous Optimization

24/7 Coverage

We Provide Managed Service Across:

Data & Analytics

Find out more...

App Design & Development

Find out more...

AI & Machine Learning

Find out more...

We Build With

We Build On

Case Studies

iRobot

Nuvei

Capitalogix Trading

Articles

Amazon EC2 Cost Savings at PRM

How to avoid hidden AMAZON Glacier charges

Data, People and Security – Can They Work Together?

Drive Your Business Forward!