Use Cases & Case Studies

MANTA Cases: Market Data Tracking

We have created a new series for you on our blog: MANTA Cases. Real-life examples of how to use MANTA are what interests our readers and prospective customers the most, therefore we have decided to periodically publish some of the most interesting and creative cases on our blog. You will be able to find the entire series in the category called “Use Cases & Case Studies” on the right side of the page. Enjoy!

We have created a new series for you on our blog: MANTA Cases. Real-life examples of how to use MANTA are what interests our readers and prospective customers the most, therefore we have decided to periodically publish some of the most interesting and creative cases on our blog. You will be able to find the entire series in the category called “Use Cases & Case Studies” on the right side of the page. Enjoy!

The first part of our series is dedicated to using MANTA for market data tracking. This interesting way of using MANTA was thought up by a company that provides analysis and reporting services to their customers. Such companies handle large amounts of data. They themselves also need to buy enough data for their analyses and reports from other companies, e.g. market data from Bloomberg and others.

The problem, in this case, was not a matter of personal data, therefore they did not need to comply with GDPR. (Compliance projects are, by the way, one of the most common uses of MANTA.) This was data such as past and current prices of shares, market research data, and other data that has a different kind of “sensitivity”.

Here, there were two main problems with the sensitivity of the data:

  1. When the company buys market data from third parties, they often need to assure the seller that the data is being handled in a safe way, with minimum risk of leaking, and it usually must be used according to the terms of a license agreement for data use.
  2. The second thing to look out for in relation to this data is that such data is usually priced according to the number of users who have access to it.

Based on these two points, MANTA had to help the customer with the need to comply with the licensing agreements, to be able to prove how and where the data is being stored; and to be able to prove the number of users, to monitor the profiles of the users working with the data.

How does MANTA solve these problems?

MANTA documents what market data sets are used in which individual reports. When combined with access privileges to the individual reports, the company has a clear and documented understanding of which data set is used by ho many end-users, and they pay for the particular data set accordingly. Moreover, with people coming and leaving and any changes happening in the actual data use, the always up-to-date lineage gives them exact numbers at any point in time.

Do you have any questions about how MANTA can solve a problem at your company? Contact us at manta@getmanta.com or use the cute little bot on the right. 

MANTA and “The Quest To Automate The Ancient Database”

The title sounds like that of an opening weekend success action movie, doesn’t it? But it’s just a case that MANTA solves daily: Automating Data Lineage Visualization from old SQL based Databases. Let’s look at one of our customer’s cases with Teradata database.

The title sounds like that of an opening weekend success action movie, doesn’t it? But it’s just a case that MANTA solves daily: Automating Data Lineage Visualization from old SQL based Databases. Let’s look at one of our customer’s cases with Teradata database.

1. Problem

The customer, an international Healthcare provider, had an ancient database system. They had been using Teradata for over 30 years, within which they had accumulated over 21,000 scripts of code. Their metadata management solution was also integrated with the application Workday. This solution was regularly making changes to the environment, notifying them 6 weeks before performing the changes. This included changes to the data scheme and interferences with data publishing.

When the customer wanted to perform an impact analysis, the time needed to analyze 8 tables was 800 hours (100 man-days of work) and that was nearly impossible to achieve in the 6-week time-frame.

2. Solution

In order for the customer’s metadata management solution to work, they needed to automate the impact analysis process and the implementation of changes to the data warehouse structure so that it would be possible to perform the structural changes across the entire 21,000 script Teradata warehouse within the 6-week time frame. MANTA was pretty much their only hope.

3. Result

After deploying MANTA, the customer was able to successfully automate the impact analysis processes as well as the search for and implementation of changes to the data warehouse structure from the integrated Workday application.

MANTA gave the customer a comfortable user interface, seamlessly integrating all their metadata management applications, including EDC, and improved the overall state of the customer’s data governance projects.

This case study is also available here to share and download in PDF.

Any questions? We’re here for you at manta@getmanta.com, as always.

MANTA + Informatica: The Ultimate Compliance Strategy

The financial industry is known for being stitched through with tons of regulations, and it is indeed challenging to comply with them. Banks and financial institutions reach out for tools to automate the process as much as possible. Read how MANTA and Informatica’s tech bond creates the ideal compliance solution.

The financial industry is known for being stitched through with tons of regulations, and it is indeed challenging to comply with them. Banks and financial institutions reach out for tools to automate the process as much as possible. Read how MANTA and Informatica’s tech bond creates the ideal compliance solution.

Most of the regulations that are currently used in banking were invented to protect the industry after the global financial crisis of 2007-2008. One of the most well-known regulations is BCBS 239. But all of these regulations define a huge number of principles that need to be complied with, sometimes at very high costs.

BCBS 239 and more

We can use the above-mentioned regulation BCBS 239 as an example. It consists of 14 principles that need to be fully adopted for the company to achieve the status of regulatory compliance. Complying with such a complex regulation is a challenging, company-wide issue that requires processing information across a broad range of tasks, departments, and entities within the financial institution.

But for all regulations in the industry, there is one thing that all implementation strategies have in common – the need for transparency. For example, when banks are asked to report aggregated risk metrics for BCBS 239, they are also asked to prove to regulators that risk scores are arrived at correctly by:

  1. Showing end-to-end data lineage from the source of the data in the data warehouse to the end report in the BI environment. This includes where the data first came from (its origin), what transformations were performed on it, and what processes and decisions affected its life cycle.
  2. Demonstrating Data Quality Controls: the identification, assessment, and management of data quality.
  3. Showing an enterprise-wide understanding of business concepts by tracking which data is available to which teams within the organization.

 

 

Accuracy is a top priority

One of our customers told us that when it comes to regulatory compliance, they refuse to leave the responsibility to their employees, but rather seek trust in their data governance solution. We cannot blame them, because before choosing to implement a MANTA + Informatica regulatory compliance solution, the customer had a team of more than 20 people that manually drew data lineage diagrams for more than 200 risk metrics within their institution.

Based on our experience with data lineage across various industries, we know that it is almost impossible for humans to create accurate data lineage visualization, because there is an endless amount of data and multiple dimensions that need to be incorporated, including the quality of the data, the people who are allowed to handle it, the processes that are done with it, etc. It requires the team to talk to multiple departments to get insight on a company-wide level, and it is difficult to maintain because it requires the team to keep track of even the smallest changes within the systems as well as the data warehouse.

MANTA together with Informatica purely automates the data-lineage gathering process. Informatica’s Enterprise Data Catalog, with strong machine learning abilities, can provide automatic mechanisms for data discovery across multiple internal company systems. What MANTA can then do, is automatically scan the metadata within the systems and read even the toughest segments of custom-made programming code that EDC usually has a hard time dealing with.

Then the customer can either visualize the data lineage in MANTA’s native visualization or see the data lineage within EDC, only this time, with data lineage all the way down to the column level. This way, the customer can achieve the highest level of detail possible, which otherwise would have been almost impossible to gain using only one solution. Now, whether it is an older data-lineage diagram that has been remodeled by calculating new dependencies or if it is the creation of a completely new one, the customer can be sure that the diagram is accurate and contains all the details.

Visit getmanta.com/informatica to learn even more about our tech bond.

Would you like to read more use cases like this in the future? Or details about our scanners and integrations? Let us know at manta@getmanta.com. We are always here for you.

MANTA + Informatica EDC Tech Bond

MANTA can complete the Enterprise Data Catalog data governance solution from Informatica with some really tough programming code. In the following article we will let you zoom in on all the details of this technical bond. (INCLUDING A NEW VIDEO!)

MANTA can complete the Enterprise Data Catalog data governance solution from Informatica with some really tough programming code. In the following article we will let you zoom in on all the details of this technical bond. (INCLUDING A NEW VIDEO!)

MANTA connects to Informatica EDC and enhances all of its data lineag:

  1. MANTA connects to the same sources as EDC, and scans all the scripts.
  2. Then, it automatically provides data lineage from those scripts and integrates it with EDC’s native resources.
  3. Finally, MANTA analyzes programming code that is out of EDC’s native scope, e.g. stored procedures, views, triggers and other scripts.

With MANTA, every Informatica customer can see how every procedure works, find out how the data is transformed between tables, and get complete end-to-end data lineage down to the column level.

Here you can watch our brand new video where Lukas explains how the technical bond works:

Like our videos? There are more where that came from! Check out our video section right here. And don’t forget to subscribe to our YouTube channel! 

As you have heard in the video above, MANTA can enrich Informatica’s Enterprise Data Catalog with a number of SQL scripts, that allow EDC to show much more depth and detail than it normally could. Here is a list of scripts that MANTA currently pushes into Informatica.

What MANTA currently pushes into Informatica’s Enterprise Data Catalog:

  • BTEQ scripts, stored procedures, views, and macros from Teradata
  • PL/SQL scripts, stored procedures, packages, and more, including DB links, from Oracle DB & Exadata
  • T-SQL scripts, stored procedures, and more, including linked servers, from Microsoft SQL Server, Sybase (now SAP ASE), and PDW
  • NZPLSQL scripts, stored procedures, and more from IBM Netezza
  • DB2 scripts, stored procedures, and more from IBM DB2
  • PostgreSQL scripts, views and more from PostgreSQL, Amazon Redshift and Greenplum

It’s all about the details

MANTA’s key feature is its understanding superpower. The ability to read even the most complex custom code is crucial for obtaining detailed and complete end-to-end data lineage. This can also be used for real data protection analysis, automated business lineage extraction, migration of your DWH to a different platform or the cloud, and to comply with regulations such as GDPR, Basil II/III, and many more.

For customers who have EDC’s older brother, Informatica Metadata Manager, we have a connector for IMM as well. And for customers who have neither, but enjoy the advanced ETL capabilities of Informatica PowerCenter, we are also able to provide data lineage in our own visualization.

All our supported technologies, both scanners and integrations can be found here. If there is anything else you would like to talk to us about, don’t hesitate to contact us at manta@getmanta.com

Key Features of MANTA: Its Understanding Superpower

Welcome to another episode of “Key Features of MANTA”. This time, we are focusing on a feature so mighty, we don’t hesitate to call it a superpower. An understanding superpower, that is!

Welcome to another episode of “Key Features of MANTA”. This time, we are focusing on a feature so mighty, we don’t hesitate to call it a superpower. An understanding superpower, that is!

Many, many vendors sell solutions for metadata management solutions that have many great features that MANTA can’t match. But luckily, we do have a superpower that we lend to other solutions and partners and make use of ourselves, or all of the above. It is the ability to parse tough, scary, custom programming code.

MANTA made itself in SQL, because it can read the most complex segments of SQL, including but not limited to: PL/SQL scripts, BTEQ Scripts, T-SQL scripts, TPT scripts, NZPLSQL scripts,  stored procedures, SQL overrides, Informatica PowerCenter Workflows… The list goes on and on, depending on which technology you use.

But since over time MANTA has been supporting more and more scanners and integrations, we’ve needed to adjust over time to other just as complex programming languages. With MANTA 3.22, we will release support for PostgreSQL (which means Amazon Redshift and Greenplum as well, yay!).

This not the only new language we will add to our family of supported technologies. In 2019, as you have read in the roadmap article we released, MANTA will add Java support, which will open up a whole new world of MANTA + Cloud integrations.

Besides being able to read the actual language, MANTA can differentiate between languages and parse multi-language environments automatically. Automation in combination with our code-parsing, understanding superpower automation and our code parsing, understanding superpower is what makes MANTA unlike any other solution on the market.

Is there something you would like to ask us? We are available at manta@getmanta.com

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