Real-Time Data Synchronization - Across ALM and DevOps Tools
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Real-Time Data Synchronization Across ALM and DevOps Tools
Contents
01 | Introduction
02 | The Acute Need for Data Synchronization in ALM
and DevOps
03 | Understanding the Basics - How Data
Synchronization Works?
04 | What Makes Data Synchronization So Challenging?
Integration
Standardization
Manual Effort
Performance
05 | Simplifying Data Synchronization with Klera
06 | Unsynchronized Data is Only a Symptom
Today, every business is on the path to becoming a software There are often multiple tickets raised at different times by
business, owning several IPs and proprietary software. However, not different people for the same issue. With numerous tools and
all businesses have equal expertise in software development, which dashboards in place, there is significant swivel-chair effort
itself has evolved significantly over the years. involved in tracking all duplicate issues, grouping and
prioritizing them according to severity, and allocating them to
Software development teams are hard-pressed to make frequent right persons. Teams also need to synchronize data across
releases to meet business goals and always have to be on their toes ticketing, project management, source code management,
to respond to any defects or issues in production. This is why the test automation, and all other tools involved in the
traditional demarcation between operations and development development, delivery, and support processes. Ensuring that
teams is blurring fast with DevOps practices emphasizing on the data is always up-to-date, consistent, and easily accessible
increased cross-functional collaboration. to all stake owners for improved collaboration and informed
decisions, is a big challenge.
However, such collaboration can only be successful when everyone
involved is on the same page. Teams need to capture issues in Many times missing fields, format mismatch, and duplication
real-time and identify the most appropriate personnel for issue errors lead to poor data quality and inconsistencies across
resolution. They should also be able to track the progress of tickets tools and processes. In this ebook, we will discuss what makes
and make escalations if required. In large organizations, it is easier data synchronization in Application Lifecycle Management
said than done. (ALM) and DevOps critical, and how organizations can ensure it
using a modern approach.
©2021 Klera, LLC. All Rights Reserved.
The Acute Need for Data Synchronization in ALM and DevOps
It is seen that delivery leaders and analysts spend crucial hours Before we proceed to how organizations can synchronize data
browsing through multiple tools and documents and performing across all ALM and DevOps tools, it will be helpful to
rigorous data mining work manually. Whether one is tracking understand the basics of synchronization, which we will
requirements, approvals, and closures or measuring cost and effort, discuss in the next section.
or trying to figure out test execution and code coverage — it all
depends on data quality.
Many times the data is inconsistent, imprecise, subjective, and hard
to collate. Moreover, it is not simple to unify data from different tools.
This restricts visibility into development and delivery processes and
affects release predictability.
Data synchronization is also crucial for defect management. It High Quality Data
ensures that when any defect is logged by someone at the Quality
Check (QC) end, then all relevant details of the defect are mapped
directly into the systems used by developers for tracking issues,
requirements, stories, and so on. Teams need to integrate processes,
standardize data, and generate consolidated cross-tool reports while
ensuring the data is up-to-date and high-quality.
Recency
Reliability Accuracy Comparability
Relevance
Validity High Availability
Completeness
Uniqueness
Consistency
©2021 Klera, LLC. All Rights Reserved.Understanding the Basics - How Data Synchronization Works?
Data synchronization is the process of frequent or real-time updating Further, all sources of data should support data versioning so
of data between two or more sources, databases, devices, etc. There that if there is a change, it should be traceable with the
are different methods to achieve data synchronization, but the most corresponding counter/timestamp. While there are databases
popular ones depend on comparing two sources and only updating which are synchronized on defined intervals, in most
the required fields to minimize bandwidth requirements and improve Relational Database Management Systems (RDMS),
efficiency. Hence, to synchronize data, there is an involvement of synchronization is triggered by add/edit/delete operations.
two components: Such real-time data synchronization is a crucial requirement
in software development projects.
Source Data: It is the component which initiates synchronization
Destination Data (also called target): It is the component to which
the changes are introduced It is important to understand that synchronization is easier to
achieve when the source and destination have similar
structures. Therefore, before integrating data from different
In many implementations (including DevOps and ALM), the source
tools and sources, organizations need to validate the data for
and destination data stores aren’t fixed as the data is often captured
correct structure and format to support mapping. In case there
from different applications or tools at different points in time. This
are differences in length, size, precision, format, additional
means synchronization has to be a bi-directional process offering
work is required to resolve these differences. That’s why
required control over the movement of data from one tool to one
synchronizing data sources with different structures becomes
or more tools.
a complex challenge.
©2021 Klera, LLC. All Rights Reserved.What Makes Data Synchronization
So Challenging?Those who understand technology cannot help but observe that in the digital world, most things are held together with duct tape and
baling wire; it’s a miracle that anything works at all. The same observation also holds true for ALM.
Integration
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There are various tools involved in supporting distributed teams that work on different aspects of software at the same time. While these
tools are supposed to work in tandem across the planning, development, staging, and deployment stages, they often lack proper
integration. Many times misconfiguration of tools leads to data syncing errors.
©2021 Klera, LLC. All Rights Reserved.Standardization Why do I have to fill all of this out?
Half of it isn’t relevant to me!
Even when easy integrations are available, creating an environment
with accurate mapping and transformations isn’t simple. Making sure
that data fields are mapped correctly becomes increasingly difficult
when projects are using different workflows, custom fields, and
values. Similar issues also arise when Quality Assurance (QA) and
development teams use different tools for defect management.
Manual Effort
A commonly encountered pattern in large organizations is that they
over-customize their project management tools with various dd-ons,
custom fields, rules, and policies. Teams are often overburdened with
this overhead. They have to spend several productive hours just
filling various fields, which can lead to oversights and errors. Further,
too many custom fields can also lead to performance issues. This is
particularly true for project management tools, where response times
for searching, viewing, updating issues, adding comments, etc.
increase as the custom fields increase.
Performance
ALM implementations often suffer from the problem of plenty. There
are various add-ons available to customize and solve specific
challenges. However, such add-ons start posing performance and
stability issues. For instance, it is seen that performance problems
arise when QA steps are managed over JIRA using a test management
add-on. As a result, JIRA instances take a longer time for the
processing of scheduled jobs and indexing and real-time syncing
of data.
©2021 Klera, LLC. All Rights Reserved.Simplifying Data Synchronization
To resolve various challenges related to data mismatch, organizations need automated bi-directional sync across various ALM and DevOps
tools. Let’s understand how Klera simplifies this task with an example:
Synchronizing Ticketing and Project Management Tools
In most organizations, it is not uncommon for teams to raise multiple The synchronization allows teams to:
or duplicate tickets for the same issue using ticketing tools Expedite tracking, qualification, and prioritization of
(ServiceNow, Zendesk, ZohoDesk, etc.). Further, support teams need support tickets
to manually assign tickets and create corresponding issues in the Automatically identify and group duplicate tickets
project management tool (Jira, VersionOne, Rally, etc.).
Automatically create corresponding issues
for resolution
With Klera, teams can automatically synchronize data across all such Update periodic status in both tools to ensure
ticketing and project management tools. It offers out-of-the-box SLA compliance
connectors, which can collect data from multiple tools, platforms,
and databases. These connectors are built using open APIs and Further, in case there’s a tool for which a connector isn’t
support bi-directional connectivity to write-back to native available, organizations can build it using Klera’s no-code
data sources. platform. Klera also offers Machine Learning capabilities, with
which organizations can intelligently assign tickets to the right
team members.
Bi-Directional Data Sync
Across Tools
©2021 Klera, LLC. All Rights Reserved.Unsynchronized Data is Only a Symptom
While in this ebook, we have focused on improving data As a result of these teams and tools silos, it becomes a full-time
synchronization, it is crucial to note that unsynchronized data is a responsibility of senior managers to keep track of all sprints,
symptom of a much bigger problem. In a typical organization, epics, themes, and so on. They need to understand who is
Business Analysts (BAs) capture and analyze requirements over a working on what, why, and how it impacts the delivery
period, on the basis of which multiple cycles of development and schedules. In the absence of automated tools delivering
testing are initiated along with frequent changes to scope and end-to-end data-driven visibility, they have to rely on
requirements. However, due to the disjointed nature of various tools cumbersome manual analysis. Needless to say, such analysis
used in these processes, it is not possible to keep track of every is not only time-consuming but can also be biased and
action, and it’s difficult to ascertain if a particular build has all the error-prone. Hence organizations should focus on enabling a
latest requirements. transparent, collaborative, and connected enterprise
without data silos.
In other words, BAs don’t know which requirements are designed,
under development, or are under testing. Similarly, developers have As a no-code application development platform, Klera serves
no knowledge of actual business goals and only deal with the version various ALM and DevOps use cases to solve various challenges
made available to them via BAs. Also, testing teams working on arising from data silos. With Klera, software teams can use
specific bugs/defects fail to recognize the need for retesting any pre-built apps or develop custom no-code apps to streamline
application components. their development and delivery.
DevOps and ALM Integrations Enabled by Klera
Jira Slack Integration
Jira Slack Integration Trello Jira Integration
GitLab Jira Integration
Jira Bitbucket Integration
GoogleSheets Jira Integration Zendesk Jira Integration
Jira GitHub Integration
Jira Salesforce Integration Jira HubSpot Integration
Servicenow Jira Integration
Confluence Jira Integration
©2021 Klera, LLC. All Rights Reserved.Project Management Design & Modelling Coding Build
Jira Enterprise Architect Eclipse ANT
Version One IBM RSA RAD Maven
Rally Rhapsody Visual Studio TFS
MS Project Matlab Simulink
Static Code Analysis Source Code
Management
FindBugs BitBucket
LDRA Git
Smart Bi-directional Connectors Enabling
SonarQube GitHub
Collaboration | Traceability | Automation | Analytics
FxCorp SubVersion
Continuous Configuration & Application Lifecycle Testing/QA
Integration Deployment Management
Bamboo Azure DevOps Azure DevOps Server Selenium
Jenkins Ansible HelixALM Cucumber
TeamCity Puppet ALM Octane TestRail
Azure DevOps Chef Polarion ALM Jira TM
Defect Management Collaboration IT Service APM
Management
Bugzilla Confluence ServiceNow AppDynamics
Jira SharePoint Zendesk New Relic
Mantis MS Exchange Nagios Dynatrace
Redmine Slack PagerDuty SalesforceWin Customer Trust With Data
Connect with a wide-range of tools and databases using dynamic,
bi-directional, smart connectors.
Schedule a Demo
KleraTM is a software products and services company focused on creating solutions that
deliver intelligence from data, unlike ever before. We enable transparent, collaborative,
and connected enterprises, without data silos. Our rapid, no code, intelligent application
development platform simplifies how you gather, analyze, and synchronize data.
www.klera.io
©2021 Klera, LLC, Los Gatos, CA. All rights reserved. Klera, Infinite Exploration, TranscendBI and the Klera logo are registered
trademarks of Klera, LLC. Other company and product names mentioned herein are trademarks of their respective companies.
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