Data lineage example

Data lineage example

In modern business intelligence BI projects, understanding the flow of data from the data source to its destination can be a challenge. The challenge is even bigger if you have built advanced analytical projects spanning multiple data sources, artifacts, and dependencies. Questions like "What happens if I change this data? They may require a team of experts or deep investigation to understand. We designed a data lineage view to help you answer these questions.

Power BI has several artifact types, such as dashboards, reports, datasets, and dataflows. Many datasets and dataflows connect to external data sources such as SQL Server, and to external datasets in other workspaces.

When a dataset is external to a workspace you own, it may be in a workspace owned by someone in IT or another analyst. External data sources and datasets make it harder to know where the data is coming from, ultimately. For complex projects and for simpler ones, we introduce lineage view. In lineage view, you see the lineage relationships between all the artifacts in a workspace, and all its external dependencies.

It shows connections between all workspace artifacts, including connections to dataflows, both upstream and downstream. Every workspace, whether new or classic, automatically has a lineage view. You need at least a Contributor role in the workspace to view it. See Permissions in this article for details. To access lineage view, go to the workspace list view. Tap the arrow next to List view and select Lineage view.

Data Lineage – Basic Elements to Understand

In this view, you see all the workspace artifacts and how the data flows from one artifact to another. You see the data sources from which the datasets and dataflows get their data.

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On the data source cards, you see more information that can help identify the source. For example, for Azure SQL server you also see the database name.

If a data source is connected via an on-premises gateway, the gateway information is added to the data source card. If you have permissions, either as a gateway admin or as a data source user, you see more information, such as the gateway name.

On datasets and dataflows, you see the last refresh time, as well as if the dataset or dataflow is certified or promoted. If a report in the workspace is built on a dataset or a dataflow that is located in another workspace, you see the source workspace name on the card of that dataset or dataflow. Select the name of the source workspace to go to that workspace. To see more metadata on any artifact, select the artifact card itself. Additional information about the artifact is displayed in a side pane.Data Lineage is defined as the life cycle of the data.

Data Lineage shows the complete data flow from origin to destination. Data lineage is the process of understanding, documenting and visualizing the data from its origin to its consumption.

This life cycle includes all the transformation done on the dataset from its origin to destination. Data lineage gives a better understanding to the user of what happened to the data throughout the life cycle. It also enables companies to trace the errors, implementing changes in the process and implementing system migration to save time and resources for efficiency.

Data Lineage helps the user to make sure if the data is coming from a reliable data source, transformations are done appropriately and loaded correctly to the designated location.

Data Lineage plays an important role where key decisions rely on accurate information.

Data lineage diagrams: A paradigm shift for info architects

Without appropriate technology and processes in place tracking, data can be virtually impossible or at the very least a costly and time-consuming endeavor. Data lineage enables the tracking of the data stream from both endpoints to ensure the data is accurate and consistent.

It allows the user to look for the data in both directions forward and backward between origin to destination of the data. ETL job is a function where we need to extract data from any defined data source and put it into another location after applying some data transformation on the collected data.

It also enables us to check for any changes in some of the data fields such as column deletion, renamed or added. It is called Impact Analysis. While dealing with complex reports, it helps in the identification of the data source which should be used in that report.

To play the role of a data steward, the person needs to know everything about the data which is being used in an organization. Data lineage helps the person to identify the least and most usable data assets in an ETL job.

Data lineage provides transparency to the user who is responsible for that particular data asset. Data lineage helps a business user to find the reports based on any particular data fields or column. Example: there is some data source that includes data fields named sales and gender if the user needs to find the reports of the bases of these data fields.

Data Lineage can help the business user to check whether the data is accurate or not. When we need to troubleshoot for any of the wrong reports, lineage can help us to identify which process and jobs are involved in creating that particular report.

In the case when we have some failed jobs, data lineage can help us to find the target tables and fields affected which are being used in the reports. One of them is who is using the data and where? When we have the visuals of the data lineage it is easy for us to find out the answers to these questions. From the data lineage graph, we can track this and find out who is using this data. There is also some parameter which needs to define at the time of data creation.

The data owner has the responsibility to store the data into the appropriate location and to grant access to the data. Know the owner of data is most important as it gives clarity that who is maintaining that data and to whom the user should contact in case of any problem with the correction.

We always need to define some access policies to the data. And before that, it is also necessary to understand what information does the data contains. It helps in classification the data so that we can understand which data policies need to define against the data so that we can protect our sensitive data. In an organization, the data is used to create several reports. These reports are used to make decisions for the growth of the organization. These reports are created by using several datasets that are generated within the organization.

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The data lineage diagram can show us which datasets are being used. So in case if we got some wrong reports this can help us to trace the source of the error if we have any. There is one more important question for the existence of data. Why does this data exist? The data which is no longer required can lead to unnecessary time and money.In the first three articles Data Lineageand we have discussed why we need data lineagewhat data lineage actually is and what are the key legislative requirements for data lineage.

In this article, I would like to discuss and give my answer to the most complicated question: how should data lineage be documented?

Before you even start thinking about documenting data lineage, there are a few crucial decisions to be made beforehand:. Horizontal data lineage represents the path along which data flows starting from its point of origin to the point of its usage. Horizontal data lineage can be documented on different data model levels such as conceptual, logical and physical. Figure 1. Usually, all companies start their journey with descriptive data lineage.

What does descriptive data lineage mean? Descriptive data lineage means that you make a description of data lineage manually using one or another application. There are some well-known data governance applications such as Axon by Informatica or Collibra. Regardless of the tooling you choose, there are several common features of descriptive data lineage:. Automated data lineage means that you automate the process of recording of metadata at physical level of data processing using one of application available on the market.

You can find an extended list of providers of such a solution on metaintegration. The company provides meta integration components to major providers of the metadata lineage function. Of course, this kinds of solutions sound very attractive. But before choosing which one you want to use, keep in mind the following:. Different groups of stakeholders have different requirements for data lineage.

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There are at least two key stakeholder groups: IT technical professionals and business users such as financial and business controllers, business analysts, auditors. The key expectations of business users are the ability to follow changes in data values and the ability to get historical information on data processing up to months in the past. The automated data lineage is basically data processing design documentation. Strictly speaking, data lineage has nothing to do with such requirements.

Hopefully I have managed to give you an overview of what documenting data lineage looks like, and what challenges it encompasses.

The Basics of Data Lineage

If you are brave enough to proceed, in the next and final article of this series I will provide you with a few tips how to start and run the data lineage implementation project. Let me say your last sentence :. Is probably wrong today: our iGovernance Suite in addition to be completely automated does historicize physical processes runned over the time one of our customers has 2 y processes stored and this becouse it use both tech and operational metadata.

This gives also the real added value to support differents areas than CDO in their daily job for operation or sw development data lineage represent just documentation, out iGivernance Suite solve them real problems reducing costs and increasing quality and SLA. Save my name, email, and website in this browser for the next time I comment. You can find out more about which cookies we are using or switch them off in settings.

This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.

Strictly Necessary Cookie should be enabled at all times so that we can save your preferences for cookie settings.These cookie jars included images of pigs, mice, goats, sheep, Humpty Dumpty and a large panda. So why did this collection of kitschy cookie jars sell for so much money? The answer? Because they belonged to legendary Pop artist, Andy Warhol. As seen in the cookie jar example, provenance adds tremendous monetary value to an artwork. Objects owned by a celebrity, a historical figure, a prestigious collector, a reputable gallery, or a top museum will garner more value at auction than works without these same credentials.

Furthermore, authenticity, context and accuracy are crucial in the art world. With art forgeries and the unlawful seizure of art by the Nazis during World War II, it is pivotal that a work of art contains a legitimate and accurate provenance. For example, the Monument Men relied on provenance in the s to track down the rightful owners of stolen art.

Today, gaps in provenance have led to numerous high profile legal disputes regarding the rightful ownership of many valuable artworks. All that being said, this blog post is not about art.

Rather, I am using art as a lens to understand data lineage. Like provenance, which provides context and increases the value of an artwork, data lineage provides the necessary understanding that enables Data Citizens to create valuable business insights using their data.

Data lineage provides a graph that documents and traces the interdependencies of the data in a data catalog. The lineage graph provides a roadmap of data consistency, accuracy and completeness, which enables business users to better understand and trust their data.

Data lineage makes data meaningful. It turns data into a valuable asset that drives innovation. In fact, end-to-end lineage is a necessary and crucial foundation for all data-driven initiatives. We are excited to announce Collibra Lineageour native, automated lineage capability that is the integration of SQLdep into Collibra Catalog. Collibra Lineage automatically maps relationships between data points to show how data moves from system to system and how data sets are built, aggregated, sourced and used — providing complete, end-to-end lineage visualization.

Before automated technical lineage, IT spent countless hours manually mapping the relationships between data. This time-consuming task prevented IT from focusing on strategic initiatives.

Luckily, Collibra Lineage combats this problem by automatically extracting lineage information from source systems and creating data flow visualizations. Integrated with Collibra Catalog, Collibra Lineage enables business users to immediately hone in on the data they care about and have full confidence in using that data to drive business decisions. Collibra Lineage solves the problem of manually mapping your data flows.

With Collibra Lineage you save valuable time by automatically extracting technical lineage from various source systems, including SQL dialects, ETL tools and BI solutions, to create an interactive data lineage map and keep it up to date. With Collibra Lineage, we generate two lineage views — business-friendly summary lineage views and detailed technical lineage views. Those interested in digging into the technical lineage can click into the technical lineage tab in Collibra Catalog to see a full technical lineage diagram.

data lineage example

From there, they can drill down into the lowest level of granularity and view column-level lineage and transformation logic. And we enhance the intelligence you can derive from your data by also mapping indirect lineage. With Collibra Lineage, you can view indirect relationships that influence the movement of data, but do not directly participate in data movement itself, such as conditional statements and joins.

Furthermore, you can easily identify and drill down into relevant table and column-level SQL code, both in and out, within the technical lineage diagram. And with our filtering functionality, you can filter technical lineage diagrams to show exactly what you need by choosing the attributes required for your purpose.

Once you have landed on the diagram you are looking for, you can easily export it in different file formats, such as PDF, PNG, and CSV, for seamless reporting and sharing. Technical lineage gives IT crucial visibility into data pipelines. With Collibra Lineage, IT can quickly and seamlessly see these relationships, while also keeping the lineage diagrams up to date. Collibra Lineage helps you understand the full context of your data by showing the flow of data as it moves from source to destination.

Without lineage, the business cannot be sure that the data they are using in their analysis comes from trusted sources and is accurate.Data Lineage is an essential component in all business metadata management. Often overlooked, the value of data lineage can be seen in many areas.

There is a growing interest in data lineage for many reasons, across all areas of the enterprise data management community, especially as business metadata becomes more necessary to non-IT professionals. There are several groups of stakeholders within any company that might be interested in data lineage. Formerly, only the Information Technology IT department understood the concept of data lineage and its value. As the explosion of data has affected every business area, business stakeholders have embraced the need for data lineage.

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Stakeholders in finance and risk have become the biggest data lineage enthusiasts. End-to-end data flows illustrate where the data originated, where it is stored and used, and how it is transformed as it moves inside and between diverse processes and systems.

Therefore, these terms often are used interchangeably. Data lineage is a description of the path along which data flows from the point of its origin to the point of its use.

Still, the definitions say nothing about documenting data lineage. To understand the way to document this movement, it is important to know the components that constitute data lineage. Data lineage components The same guides give clarification on data lineage component. TOGAF 9. Rather, it refers to the concept of a data lifecycle.

Many specialists consider data lineage as the ultimate remedy to meet these requirements. All conclusions about the necessity of data lineage are based on careful investigation of legislation requirements and consequent matching of these requirements to the data management methods and techniques, with data lineage forming part of it. Very often, a company deals with different types of business changes, such as changes in information needs and requirements, changes in application landscape, organizational changes etc.

As an example, consider a change in a database of a business application. Usually, data is transformed and processed through the chain of applications, as noted in Figure For convenience, the chain consists of just a few applications, but in reality, especially in large companies, such chains consist of dozens of applications.

In this case, data lineage will be able to ease the impact analysis of the change. For example if changes touch, information and reporting requirements the end point of the chain in Figure 1professionals will need to use root-cause analysis that will allow them to assess which data is required to produce this new information, where data should come from and how it should be transformed.

In such a case, a root-cause analysis will be much easier to do if the data lineage is already recorded. Usually, knowledge about data processing is kept in the minds of professionals or in the best-case scenario, on local computers in the form of Word or Excel documents.

Data quality In many organization, there are there are a variety of initiatives around the quality of data. In large international companies, a major data quality program may require several years for development and implementation, and longer for the user community to judge it successful. Unfortunately, many business stakeholders and IT staff do not understand the essential part that accurate data lineage plays in resolution of data quality issues.

For example, data lineage plays the key role in performing root-cause analysis while investigating data quality issues.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Data lineage is defined as a data life cycle that includes the data's origins and where it moves over time.

data lineage example

It describes what happens to data as it goes through diverse processes. It helps provide visibility into the analytics pipeline and simplifies tracing errors back to their sources. Data provenance documents the inputs, entities, systems, and processes that influence data of interest, in effect providing a historical record of the data and its origins.

It seems that both concepts are talking about about where the data comes from but I'm still confused about the differences. Are both the concepts the same? If they are different, can someone shares an example?

From our experience, data provenance includes only high level view of the system for business users, so they can roughly navigate where their data come from.

It's provided by variety of modeling tools or just simple custom tables and charts. Data lineage is a more specific term and includes two sides - business data lineage and technical data lineage. Business lineage pictures data flows on a business-term level and it's provided by solutions like Collibra, Alation and many others. Technical data lineage is created from actual technical metadata and tracks data flows on the lowest level - actual tables, scripts and statements. It links to collections of academic and industry work on provenance.

To succinctly answer your question: in generalthere's not enough context known to differentiate between data lineage and data provenance.

Within a specific context, you could look for, or create, specific and possibly different, definitions. Data Provenance is the point of origin for the data term, Data Lineage is the complete data transformation journey from point of origin to current observation point in system. In a Technical sense, that's a whole lot of baggage to start adding onto data as it flows from system to system. There has to be some HUGE justification to carry that mountain around and for what purpose?

To see some pretty graphs? Not going to happen in large real world environments. No way Learn more. What are the differences between Data Lineage and Data Provenance? Ask Question.

Asked 3 years ago. Active 5 months ago. Viewed 40k times. From wiki, Data lineage is defined as a data life cycle that includes the data's origins and where it moves over time. They are quite possibly the same thing.

data lineage example

I'd never heard of data provenance before. McDermaid Apr 13 '17 at Active Oldest Votes. Jan Andrs Jan Andrs 2 2 bronze badges. Nicholas Car Nicholas Car 3 3 bronze badges. Data Provenance is, data lineage what is the genealogy,history of its journey, where did it begin, how did it come into being, how did it change over time, where has it been, systems it has traveled, any loss or gain i.

Sam M Sam M 11 1 1 bronze badge.Data lineage is generally defined as a kind of data life cycle that includes the data's origins and where it moves over time. This term can also describe what happens to data as it goes through diverse processes. Data lineage can help with efforts to analyze how information is used and to track key bits of information that serve a particular purpose.

One common application of data lineage methodologies is in the field of business intelligence, which involves gathering data and building conclusions from that data.

Data lineage helps to show, for example, how sales information has been collected and what role it could play in new or improved processes that put the data through additional flow charts within a business or organization.

All of this is part of a more effective use of the information that businesses or other parties have obtained. Another use of data lineage, as pointed out by business experts, is in safeguarding data and reducing risk. By collecting large amounts of data, businesses and organizations are exposing themselves to certain legal or business liabilities.

These relate to any possible security breach and exposure of sensitive data. Using data lineage techniques can help data managers handle data better and avoid some of the liability associated with not knowing where data is at a given stage in a process.

Toggle navigation Menu. Data Lineage. Definition - What does Data Lineage mean?

data lineage example

Techopedia explains Data Lineage One common application of data lineage methodologies is in the field of business intelligence, which involves gathering data and building conclusions from that data.

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