All-In-One Data Fabrics Knocking on the Lakehouse Door
The fact IBM, HPE, and Microsoft made such similar data fabric and lakehouse
announcements indicate there is strong market demand, Patel says. But it’s
also partly a result of the evolution of data architecture and usage patterns,
he says. “I think there are probably some large enterprises that decide,
listen, I can’t do this anymore. You need to go and fix this. I need you to do
this,” he says. “But there’s also some level of just where we’re going…We were
always going to be in a position where governance and security and all of
those types of things just become more and more important and more and more
intertwined into what we do on a daily basis. So it doesn’t surprise me that
some of these things are starting to evolve.” While some organizations still
see value in choosing the best-of-breed products in every category that makes
up the data fabric, many will gladly give up having the latest, greatest
feature in one particular area in exchange for having a whole data fabric they
can move into and be productive from day one.
Shift Left With DAST: Dynamic Testing in the CI/CD Pipeline
The integration of DAST in the early stages of development is crucial for
several reasons. First, by conducting dynamic security testing from the onset,
teams can identify vulnerabilities earlier, making them easier and less costly
to fix. This proactive approach helps to prevent security issues from becoming
ingrained in the code, which can lead to significant problems down the line.
Second, early integration of DAST encourages a security-focused mindset from
the beginning of the project, promoting a culture of security within the team.
This cultural shift is crucial in today’s cybersecurity climate, where threats
are increasingly sophisticated, and the stakes are higher than ever. DAST
doesn’t replace other testing methods; rather, it complements them. By
combining these methods, teams can achieve a more comprehensive view of their
application’s security. In a shift left approach, this combination of testing
methods can be very powerful. By conducting these tests early and often, teams
can ensure that both the external and internal aspects of their application
are secure. This layered approach to security testing can help to catch any
vulnerabilities that might otherwise slip through the cracks.
First known open-source software attacks on banking sector could kickstart long-running trend
In the first attack detailed by Checkmarx, which occurred on 5 April and 7
April, a threat actor leveraged the NPM platform to upload packages that
contained a preinstall script that executed its objective upon installation.
To appear more credible, the attacker created a spoofed LinkedIn profile page
of someone posing as an employee of the victim bank. Researchers originally
thought this may have been linked to legitimate penetration testing services
commissioned by the bank, but the bank revealed that to not be the case and
that it was unaware of the LinkedIn activity. The attack itself was modeled on
a multi-stage approach which began with running a script to identify the
victim’s operating system – Windows, Linux, or macOS. Once identified, the
script then decoded the relevant encrypted files in the NPM package which then
downloaded a second-stage payload. Checkmarx said that the Linux-specific
encrypted file was not flagged as malicious by online virus scanner
VirusTotal, allowing the attacker to “maintain a covert presence on the Linux
systems” and increase its chances of success.
From data warehouse to data fabric: the evolution of data architecture
By introducing domain‑oriented data ownership, domain teams become accountable
for their data and products, improving data quality and governance.
Traditional data lakes often encounter challenges related to scalability and
performance when handling large volumes of data. However, data mesh
architecture solves these scalability issues through its decentralized and
self‑serve data infrastructure. With each domain having the autonomy to choose
the technologies and tools that best suits their needs, data mesh allows teams
to scale their data storage and processing systems independently.
... Data Fabric is an integrated data architecture that is adaptive,
flexible, and secure. It is an architectural approach and technology framework
that addresses data lake challenges by providing a unified and integrated view
of data across various sources. Data Fabric allows faster and more efficient
access to data by extracting the technological complexities involved in data
integration, transformation, and movement so that anybody can use it.
What Is the Role of Software Architect in an Agile World?
It has become evident that there is a gap between the architecture team and
those who interact with the application on a daily basis. Even in the context
of the microservice architecture, failing to adhere to best practices can
result in a tangled mess that may force a return to monolithic structures, as
we have seen with Amazon Web Services. I believe that it is necessary to shift
architecture left and provide architects with better tools to proactively
identify architecture drift and technical debt buildup, injecting
architectural considerations into the feature backlog. With few tools to
understand the architecture or identify the architecture drift, the role of
the architect has become a topic of extensive discussion. Should every
developer be responsible for architecture? Most companies have an architect
who sets standards, goals, and plans. However, this high-level role in a
highly complex and very detailed software project will often become detached
from the day-to-day reality of the development process.
Rapid growth without the risk
The case for legacy modernization should today be clear: technical debt is like
a black hole, sucking up an organization’s time and resources, preventing it
from developing the capabilities needed to evolve and adapt to drive growth. But
while legacy systems can limit and inhibit business growth, from large-scale
disruption to subtle but long-term stagnation, changing them doesn’t have to be
a painful process of “rip-and-replace.” In fact, rather than changing everything
only to change nothing, an effective program enacts change in people, processes
and technology incrementally. It focuses on those areas that will make the
biggest impact and drive the most value, making change manageable in the short
term yet substantial in its effect on an organization's future success and
sustainable in the long term. In an era where executives often find
themselves in FOMU (fear of messing up) mode, they would be wise to focus on
those areas of legacy modernization that will make the biggest impact and drive
the most value, making change manageable in the short term yet substantial in
its effect on an organization’s future success.
Data Fabric: How to Architect Your Next-Generation Data Management
The data fabric encompasses a broader concept that goes beyond standalone
solutions such as data virtualization. Rather, the architectural approach of a
data fabric integrates multiple data management capabilities into a unified
framework. The data fabric is an emerging data management architecture that
provides a net that is cast to stitch together multiple heterogeneous data
sources and types through automated data pipelines. ... For business teams, a
data fabric empowers nontechnical users to easily discover, access, and share
the data they need to perform everyday tasks. It also bridges the gap between
data and business teams by including subject matter experts in the creation of
data products. ... Implementing an efficient data fabric architecture is not
accomplished with a single tool. Rather, it incorporates a variety of technology
components such as data integration, data catalog, data curation, metadata
analysis, and augmented data orchestration. Working together, these components
deliver agile and consistent data integration capabilities across a variety of
endpoints throughout hybrid and multicloud environments.
Data Lineage Tools: An Overview
Modern data lineage tools have evolved to meet the needs of organizations that
handle large volumes of data. These tools provide a comprehensive view of the
journey of data from its source to its destination, including all
transformations and processing steps along the way. They enable organizations to
trace data back to its origins, identify any changes made along the way, and
ensure compliance with regulatory requirements. One key feature of modern
lineage tools is their ability to automatically capture and track metadata
across multiple systems and platforms. This capability removes the need for
manual, time-consuming documentation. Another important aspect of modern data
lineage tools is their integration with other technologies such as metadata
management systems, Data Governance platforms, and business intelligence
solutions. This enables organizations to create a unified view of their data
landscape and make informed decisions based on accurate, up-to-date information.
The Impact of AI Data Lakes on Data Governance and Security
One of the primary concerns with AI data lakes is the potential for data silos
to emerge. Data silos occur when data is stored in separate repositories or
systems that are not connected or integrated with one another. This can lead to
a lack of visibility and control over the data, making it difficult for
organizations to enforce data governance policies and ensure data security. To
mitigate this risk, organizations must implement robust data integration and
management solutions that enable them to maintain a comprehensive view of their
data landscape and ensure that data is consistently and accurately shared across
systems. Another challenge associated with AI data lakes is the need to maintain
data quality and integrity. As data is ingested into the data lake from various
sources, it is essential to ensure that it is accurate, complete, and
consistent. Poor data quality can lead to inaccurate insights and
decision-making, as well as increased security risks.
AppSec Consolidation for Developers: Why You Should Care
Complicated and messy AppSec programs are yielding a three-fold problem:
unquantifiable or unknowable levels of risk for the organization, ineffective
resource management and excessive complexity. This combined effect leaves
enterprises with a fragmented picture of total risk and little useful
information to help them strengthen their security posture. ... An increase in
the number of security tools leads to an increase in the number of security
tests, which in turn translates to an increase in the number of results. This
creates a vicious cycle that adds complexity to the AppSec environment that is
both unnecessary and avoidable. Most of the time, these results are stored in
their respective point tools. As a result, developers frequently receive
duplicate issues as well as remediation guidance that is ineffective or lacking
context, causing them to waste critical time and resources. Without consolidated
and actionable outcomes, it is impossible to avoid duplication of findings and
remediation actions.
Quote for the day:
"There is no substitute for
knowledge." -- W. Edwards Deming
