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Paul Singman
Paul Singman Author

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Einat Orr, PhD
Einat Orr, PhD Author

Einat Orr is the CEO and Co-founder of lakeFS, a...

Last updated on February 26, 2024

What is Data Lifecycle Management?

Datasets are the foundational output of a data team. They do not appear out of thin air. No one has ever snapped their fingers and created an orders_history table.

Instead, useful sets of data are created and maintained through a process that involves several predictable steps. Managing this process is often referred to as data lifecycle management

To put it another way, data lifecycle management encapsulates two things:

  1. What is required to publish new data?
  2. What is required to ensure published data is useful?

The most effective data teams have a clear understanding of the steps and processes their data is subject to en route to publication. Less effective teams tend to operate reactively, patching the latest issue without a higher-level perspective.

The data lifecycle management process

The Importance of DLM

The massive rise in data means that companies are keeping information in more places and on more platforms than ever before.

The demand for DLM is obvious. Here’s why data lifecycle management is so important:

1. Security
One of the primary purposes of data lifecycle management is to ensure that data is always protected. DLM guarantees that private, confidential, or sensitive data is always safeguarded against potential breaches, theft, or compromises. DLM provides an end-to-end method for securing sensitive information from internal and external threats.

2. Data integrity
A good DLM approach should be able to preserve any data in its original form, trace any modifications, and provide insight to key decision-makers. Data should be accurate and dependable regardless of where it is kept, who uses it, or how many copies exist. Maintaining data integrity guarantees that the information utilized is complete, correct, and safe to deal with.

3. Data availability
Data is meaningless if it’s not available for usage by teams inside your business, but excessive availability can be problematic if not controlled. Approved users should be able to access data wherever and whenever they need it, without disrupting workflows or day-to-day operations.

Necessity Borne Out of Complexity

One size for managing a data’s lifecycle does not fit all. Instead, it is true that as the complexity of the data environment increases, adding functionality to provide additional guarantees proves more critical.¹

In other words, when things are simpler, you can get away with a more laissez-faire approach to lifecycle data management. 

With this mind, we’ll start by discussing each step of the data lifecycle from a simple perspective and work our way up to what can be added to deal with complexity.

¹As measured by volume of data, No. of jobs, No of people collaborating over, etc

What are the Goals of Data Lifecycle Management?

DLM has three key goals: secrecy, integrity, and availability, commonly known as the CIA triad.

Confidentiality

Today, organizations share vast amounts of data. This raises the likelihood of data loss and abuse. As a result, data security and confidentiality are critical in protecting sensitive information, such as financial records, business plans, personally identifiable information (PII), and so on, against unauthorized access and cyberattacks.

Integrity

Multiple people access, use, and share data in an organization’s storage systems. When data is in use, it is inevitably subjected to numerous alterations and adjustments. An organization’s DLM strategy must guarantee that the information supplied to users is correct, up-to-date, and dependable. As a result, one of the aims of a DLM strategy is to ensure data integrity by safeguarding it when in use, in transit, and when stored.

Availability

Protecting and maintaining data integrity is crucial, but it’s useless if users can’t access it when needed. Data availability is vital in today’s 24/7 global business climate. DLM attempts to guarantee that data is available and accessible to users when they need it, allowing important business functions to proceed without interruption.

The Different Phases of Data Lifecycle Management

Instead of taking a “project management-y”  perspective of creating entirely new datasets, I prefer to think in terms of the journey new records of data take to become part of a production dataset.

We break it down like so:

  1. Data Ingestion — Bringing raw data into the data environment
  2. Data Transformation — logic applied to landed data to produce clean datasets
  3. Testing & Deployment — Quality/validation tests applied during data publication
  4. Monitoring & Debugging — Tracking data health and finding cause of errors

The processes depicted in the above diagram aren’t created and run once, producing datasets that exist statically thereafter. Instead, it’s a constantly running and evolving system that cycles every day, hour, or even second. 

Let’s discuss in more detail!

Data Ingestion

New data needs to come from somewhere, and data ingestion is the process of both data sharing and bringing new data into the analytics environment.

The most basic strategy is to land raw data in a warehouse or object storage, often partitioned by date. Once the raw data is stored, it becomes available to downstream transformational processes that turn it into comsumable datasets.

In simpler environments where some data downtime is tolerated, this strategy suffices since the time spent recovering from bad data that makes its way downstream is not prohibitive. For example, with a small enough dataset you can drop and recreate an entire production table in a blink.

As that “scorched table” approach becomes less feasible, the ingestion lifecycle can evolve to include validation tests executed over ingested data. 

Managing the dependency between ingesting, testing, and transforming tasks is often the responsibility of an orchestration tool like Airflow. If validation tests fail, downstream transformations won’t execute, preventing low-quality data from polluting production.

As the number of datasets and tests increases, even this strategy will reach the limit of its manageability. If you find yourself in this situation, it is strongly recommended that you take a different approach to your data collection & ingestion with stronger isolation guarantees.

continuous integration
Diagram of ingestion to an isolated branch of raw data.

As shown above, the most resilient approach lands new data onto an isolated ingest branch sourced from the raw data. Validation tests are executed against the isolated ingest branch, and only upon passing, does new data become part of the raw dataset.

Critically, downstream jobs can now read from the raw data without first checking the state of a testing dependency. This eases the burden on the orchestration tool and reduces the brittleness of the overall system.

Data Transformation

It is never the case that the raw data ingested is in the ideal final form for later analysis. Instead, transformations of the data are required that do some or all of the following:

  • Standardize Fields
  • Resolve Duplicates
  • Calculate time-based aggregations
  • Apply business logic

In terms of managing data’s lifecycle, an important idea in data transformations is traceability. Given a row of transformed data, how easy is it to figure out:

  1. From which raw dataset it came and what was that dataset’s state at the time of execution?
  2. Which process transformed it and what logs did it generate?

If answering the above questions is difficult, you are susceptible to spending more time than is ideal figuring out what’s happening. Figuratively speaking, your data’s lifecycle will grind to a halt.

From my experience in simpler environments, the answer to these questions is simply remembered by the people working in it. As a fallback, making use of the UI’s provided by tools like Airflow or dbt to visualize job and data dependencies is helpful.

At a certain point, adopting a data observability or cataloging tool becomes worth the investment.

Testing & Deployment

A smart data lifecycle will include some sort of process for deployment… other than thoughtlessly adding data to production and hoping for the best. 

The tried and true strategy used in application development for continuous deployment also works for data. The recommended approach is to implement a CI/CD-type workflow with a combination of unit, data quality, and integration tests as part of the deployment process.

At lakeFS, we strongly believe in enabling this type of workflow through the use of merging operations and pre-merge hooks. If you do not test your transformed data before exposing it to data consumers, you’re leaving the fate of your data quality to chance.

Monitoring & Debugging

At the risk of sounding like a data hypochondriac, the health of the data environment and the datasets it produces need constant monitoring.

A goal to aim for is to proactively catch issues in the data before external users of the data do. It is more reassuring to see a note in a dashboard saying “we’ve identified an error in this data and are working on a fix” than having the CEO of your company find it herself.

In order for this to happen, failures in job execution need to be captured and sent as high- or low-level alerts. Tests that check for unexpected changes in distribution (like the percentage of Android users in the population) should run periodically over critical datasets.

These strategies help to catch the errors; the next step is to fix them. In smaller environments, you can get away with manually running operations like deleting a partition in an object store or running a “delete from” query against the warehouse.

Although they get the job done, both are prone to human error and risk compounding an issue further. As complexity increases, the ability to revert data in the simplest way possible becomes a necessity instead of a nice-to-have. 

A data environment that either doesn’t catch errors or isn’t simple in reverting them risks breaking the data lifecycle, preventing new development from even starting.

What are the Benefits of Good Data Lifecycle Management?

The goal of Data Lifecycle Management is to improve the practice of data practitioners by structuring how they think about the data management lifecycle. It is common to manage data flowing from many input sources, all of which combine and transform to create valuable data assets used in reporting, machine learning, and operational functions.

Given this complexity, it is imperative to use consistent processes to manage the lifecycle of these data assets, from inception to consumption. This takes the form of centralized technologies and processes for ingesting, transforming, and monitoring data quality. 

Ultimately, this lets a data team maximize its potential and impact. A data team following effective Data Lifecycle Management practices can manage more use cases of its data without getting bogged down investigating data quality or other issues.

Tools and Technologies for Data Lifecycle Management

Specialized tools can help organizations in efficient data management, ensuring that data is safe, accurate, and easily accessible to enable informed decision-making. Such tooling contributes to the streamlining of DLM procedures, the optimization of data storage and utilization, and the acceleration of innovation and growth.

Data Management Platforms

Data management platforms (DMPs) are systems that gather, organize, and activate data from a variety of internet, offline, and mobile channels. These data sources include first-, second-, and third-party audience data – all used to build thorough consumer profiles for targeted advertising and customization campaigns.

DMPs often deliver features such as:

  • Data integration
  • Audience building
  • Cross-device targeting
  • Automated data analysis

Data Classification Tools

Data classification tools represent another key area of data management. These are applications that recognize and categorize sensitive information within an organization. They can assign properties to each piece of data, assisting companies in recognizing and classifying sensitive material and ensuring that it’s properly protected and handled.

The disadvantage of maintaining data classification tools is that they are resource-intensive, call for expert knowledge, and frequently incur substantial costs.

Data Monitoring and Analytics

Data monitoring is a proactive process that reviews and evaluates critical company data to ensure quality and compliance with specified standards. It can apply to any stage of data management, from data creation, data collection, and data storage to data usage, data sharing, and data processing.

Data analytics, on the other hand, is the process of transforming data into insights. So, data monitoring and analytics help teams maintain high-quality data and gain relevant insights that enable informed decision-making.

Common Challenges and Solutions in DLM

Common DLM challenges include accurate resource allocation and determining strategies for accurate data gathering, storage, consumption, and administration.

To solve these difficulties, companies can use the following solutions:

  • Automation solutions – Automating data management operations is one of the most effective tactics to assure accuracy and efficiency in DLM. Automation technologies make it easier to enter, validate, transmit, and archive data.
  • Data governance protocols – Implementing adequate data governance protocols opens the door to effective data management and the development of a cohesive data strategy.
  • Strong security measures – Security is a key aspect of data management, especially when it comes to handling sensitive data. Organizations should use data encryption techniques to protect data, as well as firewalls or antivirus software on their systems for further data security.

Data Lifecycle Management Vs Application Lifecycle Management

Like many concepts in the data world, it borrows from a popular concept in software engineering. Data Lifecycle Management is no exception, taking its namesake from the influential ALM concept.

Software applications go through the same process of development, deployment, and eventually retirement. This leads to a great deal of overlap between the fundamental concepts in both data and application lifecycles. 

Where they differ is where it is most interesting. One main difference stems from the fact that applications are relatively small in size. Even the largest app on your phone is no more than a few hundred MB.

Data assets, meanwhile, can grow as large as hundreds of terabytes, which is much, much bigger. This makes it significantly more complicated to create copies of these data assets for testing before deployment. As a result, it is imperative to be able to clone large data objects, creating a new reference to it without duplicating the underlying objects in storage.

This provides a useful way to test even the largest data assets during deployment and lets Data Lifecycle Management more closely reflect the principles of Application Lifecycle Management.

lakeFS as the Data Lifecycle Management Tool

So far, we’ve discussed the data lifecycle at a mostly conceptual level, without getting into specific implementation details. You should, however, start to understand how lakeFS enables best practices in data lifecycle management at each step, particularly as complexity in the environment increases. 

This is realized through enabling:

  • Isolated ingestion workflows
  • Automated testing-based deployment
  • Easy data reproducibility and revert operations

For this reason, we like to think of lakeFS as the tool for adopting best practices in data lifecycle management.

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