| Case Study

How to Build a Flexible, Unified Data Stack — No Vendor Lock-in or Infrastructure Maintenance Required

The many ways Shakudo provides an operating system for data stacks — offering fast, frictionless access to data resources for all who need it — and how that helps organizations pivot towards a more analytic focus.

How to Build a Flexible, Unified Data Stack — No Vendor Lock-in or Infrastructure Maintenance Required

Author(s):
Roger Magoulas
No items found.
Updated on:
August 29, 2023

Table of contents

Many organizations are trying to become more data informed, to better leverage their data assets for decision making. Yet they are often stymied by the weight of data engineering “plumbing” required to build and maintain their data infrastructure. If most of the data stack can, in essence, disappear, data teams can turn their focus to analysis, model building, and insights, and how best to communicate their work to decision makers. How to get there? While there’s no simple path, the Shakudo platform provides a good start. In this two part post we cover the many ways Shakudo provides an operating system for data stacks — offering fast, frictionless access to data resources for all who need it — and how that helps organizations pivot towards a more analytic focus.

Part 1

Shakudo: The Operating System for Data Stacks

Introduction

In today's data-driven landscape, organizations face the challenge of managing complex data infrastructures while striving to better apply analytics to decision making. Yet many of today’s data organizations are hamstrung by fixed, inflexible data stacks and an over reliance on tactical, data engineering-centric, brittle data infrastructures that create backlogs impeding what data teams should focus on: analysis, model building, and insight creation.

Shakudo provides a new paradigm — a living, evolving operating system for data stacks. With Shakudo, organizations can easily tailor their data infrastructure to adapt to dynamic requirements and resource constraints, for example incorporating new technologies, like generative AI. The platform provides a consistent interface while supporting the evolution of tools — whether to enable migration to new platforms, align skills with tools, or adopt new technologies or tools.

The Shakudo Platform

Users access the hosted Shakudo platform via a SaaS web page that displays a palette of available tools and components. The vetted, compatible, preconfigured list of tools allows quick and frictionless access to what data professionals need to immediately start developing pipelines, running analysis, and building models, including LLMs. Think iOS or Android with an organized set of clickable icons taking the user to the most appropriate environment for the task at hand.


Choosing What You Know Best

The Shakudo platform integrates with more than [.displaycountclass]100[.displaycountclass] open source and commercial stack components, allowing data teams to pick the tools that best suit their preferences and capabilities, while accommodating any budget constraints. The continually growing list of components on offer are fully vetted by Shakudo SMEs to ensure compatibility, scalability, security, and other concerns. The result: data teams can curate and preconfigure the components available to analysts that optimize productivity, control costs, and meet governance, regulatory, and other compliance requirements, all while avoiding vendor lock-in.

From a resource perspective, the Shakudo platform's flexibility and control lets data teams offer access to tools and components that help streamline training, onboarding, recruiting, and other processes — improving productivity for anyone who needs to work with data.

Transforming Data Stacks Into Adaptive Living Systems

Shakudo helps convert traditional, fragile, and monolithic data stacks into a living, evolving platform. With Shakudo, organizations can adapt their data stacks to meet changing requirements, align tools to skills, and leverage new technologies. This adaptability ensures that data stacks remain relevant, future-proof, and responsive to the dynamic needs of the organization.

For any organization migrating elements of their data stack, Shakudo is a godsend, supporting easy access to legacy and new tools. For example, several of Shakudo’s customers have evolved their data stack multiple times over the course of the past two years without having to modify their underlying infrastructure or system architecture.

Streamline DevOps

Managing distributed data infrastructures can be a complex and time-consuming task. Shakudo alleviates this burden through a focus on DevOps automation. Adding components becomes simple via a prompt-driven configuration process — once a component is added, Shakudo ensures that all appropriate connections, settings, and permissions are set, making the component readily available to all users.

Streamlined DevOps is at the center of what helps data organizations move from having too great a focus on configuration and admin processes towards the strategic work of data analysis, model building, and generating valuable insights.

Bespoke Generative AI Models

Shakudo understands the importance of privacy and intellectual property protection. With a focus on providing open source components for building bespoke generative AI foundation models, Shakudo allows data scientists to tailor AI models to their organization's unique requirements and IP. By avoiding the limitations of large-scale commercial models, Shakudo empowers organizations to extract maximum value from their data while ensuring compliance with privacy regulations.

Democratizing Data Access and Governance

Shakudo has the potential to democratize data access throughout an organization. Decentralized organizations no longer need to build their own infrastructure, as Shakudo provides a centralized platform that ensures easier enforcement of governance standards. Organizations can empower their distributed teams with frictionless access to data resources, supporting a more decentralized approach to data and analytics. We cover this topic in more detail in Part 2.

Collaborative Analytics Through “Sessions” 

Notebooks have become a primary vehicle for advanced analytics, but they can make collaboration difficult. Shakudo’s “Sessions” uniquely supports collaborative work on common analytic tools like Jupyter Notebooks — allowing even remote analysts to work together in real-time, or sequentially across time zones. Pair analytics provides a raft of benefits, particularly on the type of complex analytics and model building done in notebook environments:

  • Improved accuracy — paired analysis encourages peer review and checking of work, reducing mistakes.
  • Deeper insights — multiple perspectives can uncover more nuanced findings than someone working on their own; building off others’ ideas fosters creativity.
  • Broader expertise — analysts with complementary skills (e.g., stats and visualization) can collaborate for a more well-rounded result; knowledge sharing builds team capability and provides upskilling opportunities.
  • Efficiency gains — paired work allows division of labor while still working together; two analysts may progress faster than one.
  • Onboarding support — pairing junior and senior analysts creates the environment for mentoring and skill development.
  • Validation — working with a partner provides real-time feedback as analysis progresses, improving quality while fostering early detection of assumptions and biases.
  • Built-in code review — improves code quality and documentation.
  • Continuity and knowledge retention — shared context fosters team rapport, spreads domain knowledge, and reduces knowledge loss when team members leave or change roles.

Shakudo’s collaborative “Sessions” helps improve analysis and creates smarter, more cohesive teams, all while encouraging an environment of continuous learning.

Gaining Control Over Data Infrastructure

The data landscape continues to grow more distributed and more complex. Organizations need a data infrastructure that can keep pace and empower analytic teams.

Shakudo provides an operating system for data stacks that helps organizations get more out of their data teams by transforming static, legacy tools into an adaptable, evolving platform. Built for continuous evolution, Shakudo future-proofs your data investments. Easily migrate from old to new, align tools to skills, and adopt new tools to meet new requirements — all while maintaining a consistent interface that provides fast, frictionless access to vetted data stack components.

Shakudo also facilitates more open, distributed, and democratized analytics across your organization, empowering decentralized teams through curated self-service access. And through Shakudo's collaborative “Sessions,” you can build institutional knowledge across remote teams while improving accuracy, trust, and insights.

Shakudo provides a platform built to meet today’s needs and tomorrow’s challenges, turning data infrastructure into a strategic asset.

Part 2

From Data Plumbing to Insight Creation: How Shakudo Transforms Data Teams

After covering the many ways Shakudo helps improve DevOps and data team productivity in Part 1 of this blog post, here we take a deeper dive into how Shakudo helps data teams shift their focus away from infrastructure drudgery and towards analytics, model building, and deriving insights. Shakudo transforms data teams from pipeline-centric to insight-driven.

By addressing data plumbing challenges, Shakudo enables analysts to spend more time on high-value tasks: designing analytic-friendly data structures, collaborating with peers, rapidly iterating, and uncovering insights. Shakudo becomes the catalyst for data teams to make this critical shift.

In Part 2 of this post, we explore the following specific ways Shakudo facilitates this transformation:

  • Democratizing access to data, with appropriate governance.
  • Moving from one-off pipeline-based data to purpose-built, designed data models.
  • Reducing data debt through documentation and shared dimensions.
  • Empowering agile analysis with self-service tools.
  • Fostering collaborative analytics for better insights.
  • Smoothing onboarding by focusing new users on functionality.

Let's dive in and see how Shakudo helps data teams embrace their true purpose: insight creation. The heavy lifting of data engineering is important, but not the end goal. Shakudo provides the platform for data teams to make analytics and modeling their central focus.

Improving Analytic Productivity 

Many organizations find they get insufficient productivity out of their data infrastructure. It’s understandable, given the challenges of managing the complex, distributed mix of legacy and modern data stacks that make up data operations. The result: Too much time and resources spent on data engineering, and not enough on analytics and insights.

Whatever an organization's mix of data stacks and tools consists of, it requires a big investment in the data engineering plumbing (configuration, connections, credentials, privileges, topology, orchestration) that connects the various components used to build a data infrastructure. A never-ending queue of requests from other teams further burdens data engineering resources. The result: data engineering resources pushed to build quickly assembled, fragile, difficult to maintain one-off data pipelines, leaving little time to prepare, transform, and organize the data for analysis. Documentation suffers, duplicity abounds, and analysts face a confusing array of poorly organized, difficult to understand options for analysts or anyone interested in using data.

The blame doesn't rest with the data engineers — they are doing their best to keep up. What if a tool existed that can help data engineers transition from building data pipelines to enabling the analyst community and other users of data to build their own pipelines without the burden of plumbing and setup? That is what the Shakudo platform provides. It’s a vehicle for pivoting data teams away from the grind of infrastructure towards their true purpose — analysis and discovery.

How does it work? Shakudo allows data engineers to set up environments appropriate for a wide variety of pipeline and analytic tasks. The analytic community then selects the tool they want, completely preconfigured with all connections, credentials, and component compatibility set. Automated DevOps makes integrating with more than [.displaycountclass]100[.displaycountclass] and counting data tools and frameworks a simpler process, ensuring the right tools are available now and in the future, with no friction or fuss.

The benefits are manifold. With Shakudo, the data community can:

  • Build pipelines without the need to install or configure components.
  • Add data quality and transformation steps that make data more usable and comprehensible.
  • Design data models geared towards analysis, sharing, and reuse.
  • Focus more on consistent naming conventions, documentation, and specifying unambiguous sources of key dimensions and facts.
  • Migrate from legacy to modern data stacks using the same platform.

Democratized Data Access

Many organizations want to embrace at least some decentralization of access to their data assets, enabling dispersed teams to become more data savvy. However, building out a decentralized data infrastructure is difficult, often creating a mish-mosh of incompatible components, wildly variant technical competence, and ambiguous, difficult to trust results. A data mess instead of a Data Mesh. Here's where Shakudo shines. The Shakudo platform provides a consistent, secure, governance-compliant set of tools that enables data professionals from anywhere in an organization to get work done, with no delay.

Embracing the Shakudo platform allows data engineers to focus on enabling decentralized organizations, rather than standing in their way, regardless of where users sit.

Plenty of other benefits accrue to an organization trying to decentralize their DataOps with Shakudo:

  • Consistent tool palette for simpler onboarding, training regimes, and easier paths to collaboration.
  • Straightforward processes to better ensure standards, conventions, data sharing, and analytic-oriented data structures.
  • Helps contain "rogue" efforts by teams to build their own data infrastructure — removes the impulse to bypass the backlogs caused when data engineering-centric organizations can't respond quickly enough to data requests.

For many organizations, decentralizing data operations is done for agility, flexibility, and productivity – keeping the data close to those who know it best. However, that same decentralization can become a burden, when organizations deem it appropriate to reorganize or if special projects require coordination across teams. Shakudo has an answer: providing a platform that helps all those who work with data to coalesce around a common set of tools, processes, and governance policies — making reorganizations, interteam transfers, and special projects that much easier to implement.

Purpose-Built Data Structures

Shakudo helps free up analytic resources to design, build, and document analysis-friendly data structures. Dimensional models, precalculated statistics, and understandable naming conventions designed for data sharing all help improve productivity where it matters: more analysis, more model building, more insights. It’s a stark contrast to the gnarly data structures associated with one-off pipelines built to meet narrow analytic needs.

Well documented data models with shared dimensions (like customer and product) are easier to understand and leverage, and create an environment for more consistent results. A boon for organizations pushing for democratizing access to data resources.


Talent Recruiting, Onboarding, and Retention

Shakudo’s adaptable data stack avoids locking organizations into tools with long ramp up times or niche talent pools. The agile platform allows organizations the flexibility to align components with resource preferences — whether via leveraging available skills, training internal teams, or hiring new experts. And if and when new tools become available, or migrations away from old tools becomes appropriate, Shakudo is there to simplify those transitions. Embracing new technologies becomes a viable recruiting strategy for companies using Shakudo, particularly tech that make an immediate difference.

Infrastructure Governance

The curated Shakudo toolbox provides a mechanism for enforcing security, privacy, and regulatory policies. Restricting users to the preconfigured components available on the Shakudo platform puts governance into the knowing hands of the data engineers who set up and install those components, reducing the peril of uncontrolled, sloppy installs and the unintended consequences of lax attention.

Pivot to Insights with Shakudo

Managing complex data infrastructure devours time, leaving insufficient bandwidth for deriving insights. Tactical pipelines proliferate. Data debt accumulates. Analytics suffers.

Shakudo transforms this vicious cycle. The platform pivots data teams from infrastructure drudgery to high-value analytics.

With Shakudo, analysts design reusable models, not one-off pipelines. Self-service access and collaboration foster agile iteration. Data is understandable and documented. Talent onboarding is smoother.

Shakudo also unlocks decentralized analytics without infrastructure sprawl. Governed, democratic data access becomes feasible, and the platform creates a consistent data experience regardless of team location.

In summary, Shakudo enables your evolution to an insight-driven data organization.

Roger Magoulas

Roger has been building data analytic teams and platforms since the mid ’90s, including stints at Sybase, San Francisco Opera, QAD, Columbia Pictures, and others. At O'Reilly, Roger built the data infrastructure, integrating internal and external data sources, and data team to help identify technology adoption trends and behavior analysis. Roger also served as co-chair for the O'Reilly Strata conference. During the pandemic, Roger served on a COVID Insights task force for the State of California and worked on a text-mining project to make historic science data more available on behalf of a non-profit — the work was featured in the journal “Nature.”
| Case Study
How to Build a Flexible, Unified Data Stack — No Vendor Lock-in or Infrastructure Maintenance Required

Key results

Data Stack

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Many organizations are trying to become more data informed, to better leverage their data assets for decision making. Yet they are often stymied by the weight of data engineering “plumbing” required to build and maintain their data infrastructure. If most of the data stack can, in essence, disappear, data teams can turn their focus to analysis, model building, and insights, and how best to communicate their work to decision makers. How to get there? While there’s no simple path, the Shakudo platform provides a good start. In this two part post we cover the many ways Shakudo provides an operating system for data stacks — offering fast, frictionless access to data resources for all who need it — and how that helps organizations pivot towards a more analytic focus.

Part 1

Shakudo: The Operating System for Data Stacks

Introduction

In today's data-driven landscape, organizations face the challenge of managing complex data infrastructures while striving to better apply analytics to decision making. Yet many of today’s data organizations are hamstrung by fixed, inflexible data stacks and an over reliance on tactical, data engineering-centric, brittle data infrastructures that create backlogs impeding what data teams should focus on: analysis, model building, and insight creation.

Shakudo provides a new paradigm — a living, evolving operating system for data stacks. With Shakudo, organizations can easily tailor their data infrastructure to adapt to dynamic requirements and resource constraints, for example incorporating new technologies, like generative AI. The platform provides a consistent interface while supporting the evolution of tools — whether to enable migration to new platforms, align skills with tools, or adopt new technologies or tools.

The Shakudo Platform

Users access the hosted Shakudo platform via a SaaS web page that displays a palette of available tools and components. The vetted, compatible, preconfigured list of tools allows quick and frictionless access to what data professionals need to immediately start developing pipelines, running analysis, and building models, including LLMs. Think iOS or Android with an organized set of clickable icons taking the user to the most appropriate environment for the task at hand.


Choosing What You Know Best

The Shakudo platform integrates with more than [.displaycountclass]100[.displaycountclass] open source and commercial stack components, allowing data teams to pick the tools that best suit their preferences and capabilities, while accommodating any budget constraints. The continually growing list of components on offer are fully vetted by Shakudo SMEs to ensure compatibility, scalability, security, and other concerns. The result: data teams can curate and preconfigure the components available to analysts that optimize productivity, control costs, and meet governance, regulatory, and other compliance requirements, all while avoiding vendor lock-in.

From a resource perspective, the Shakudo platform's flexibility and control lets data teams offer access to tools and components that help streamline training, onboarding, recruiting, and other processes — improving productivity for anyone who needs to work with data.

Transforming Data Stacks Into Adaptive Living Systems

Shakudo helps convert traditional, fragile, and monolithic data stacks into a living, evolving platform. With Shakudo, organizations can adapt their data stacks to meet changing requirements, align tools to skills, and leverage new technologies. This adaptability ensures that data stacks remain relevant, future-proof, and responsive to the dynamic needs of the organization.

For any organization migrating elements of their data stack, Shakudo is a godsend, supporting easy access to legacy and new tools. For example, several of Shakudo’s customers have evolved their data stack multiple times over the course of the past two years without having to modify their underlying infrastructure or system architecture.

Streamline DevOps

Managing distributed data infrastructures can be a complex and time-consuming task. Shakudo alleviates this burden through a focus on DevOps automation. Adding components becomes simple via a prompt-driven configuration process — once a component is added, Shakudo ensures that all appropriate connections, settings, and permissions are set, making the component readily available to all users.

Streamlined DevOps is at the center of what helps data organizations move from having too great a focus on configuration and admin processes towards the strategic work of data analysis, model building, and generating valuable insights.

Bespoke Generative AI Models

Shakudo understands the importance of privacy and intellectual property protection. With a focus on providing open source components for building bespoke generative AI foundation models, Shakudo allows data scientists to tailor AI models to their organization's unique requirements and IP. By avoiding the limitations of large-scale commercial models, Shakudo empowers organizations to extract maximum value from their data while ensuring compliance with privacy regulations.

Democratizing Data Access and Governance

Shakudo has the potential to democratize data access throughout an organization. Decentralized organizations no longer need to build their own infrastructure, as Shakudo provides a centralized platform that ensures easier enforcement of governance standards. Organizations can empower their distributed teams with frictionless access to data resources, supporting a more decentralized approach to data and analytics. We cover this topic in more detail in Part 2.

Collaborative Analytics Through “Sessions” 

Notebooks have become a primary vehicle for advanced analytics, but they can make collaboration difficult. Shakudo’s “Sessions” uniquely supports collaborative work on common analytic tools like Jupyter Notebooks — allowing even remote analysts to work together in real-time, or sequentially across time zones. Pair analytics provides a raft of benefits, particularly on the type of complex analytics and model building done in notebook environments:

  • Improved accuracy — paired analysis encourages peer review and checking of work, reducing mistakes.
  • Deeper insights — multiple perspectives can uncover more nuanced findings than someone working on their own; building off others’ ideas fosters creativity.
  • Broader expertise — analysts with complementary skills (e.g., stats and visualization) can collaborate for a more well-rounded result; knowledge sharing builds team capability and provides upskilling opportunities.
  • Efficiency gains — paired work allows division of labor while still working together; two analysts may progress faster than one.
  • Onboarding support — pairing junior and senior analysts creates the environment for mentoring and skill development.
  • Validation — working with a partner provides real-time feedback as analysis progresses, improving quality while fostering early detection of assumptions and biases.
  • Built-in code review — improves code quality and documentation.
  • Continuity and knowledge retention — shared context fosters team rapport, spreads domain knowledge, and reduces knowledge loss when team members leave or change roles.

Shakudo’s collaborative “Sessions” helps improve analysis and creates smarter, more cohesive teams, all while encouraging an environment of continuous learning.

Gaining Control Over Data Infrastructure

The data landscape continues to grow more distributed and more complex. Organizations need a data infrastructure that can keep pace and empower analytic teams.

Shakudo provides an operating system for data stacks that helps organizations get more out of their data teams by transforming static, legacy tools into an adaptable, evolving platform. Built for continuous evolution, Shakudo future-proofs your data investments. Easily migrate from old to new, align tools to skills, and adopt new tools to meet new requirements — all while maintaining a consistent interface that provides fast, frictionless access to vetted data stack components.

Shakudo also facilitates more open, distributed, and democratized analytics across your organization, empowering decentralized teams through curated self-service access. And through Shakudo's collaborative “Sessions,” you can build institutional knowledge across remote teams while improving accuracy, trust, and insights.

Shakudo provides a platform built to meet today’s needs and tomorrow’s challenges, turning data infrastructure into a strategic asset.

Part 2

From Data Plumbing to Insight Creation: How Shakudo Transforms Data Teams

After covering the many ways Shakudo helps improve DevOps and data team productivity in Part 1 of this blog post, here we take a deeper dive into how Shakudo helps data teams shift their focus away from infrastructure drudgery and towards analytics, model building, and deriving insights. Shakudo transforms data teams from pipeline-centric to insight-driven.

By addressing data plumbing challenges, Shakudo enables analysts to spend more time on high-value tasks: designing analytic-friendly data structures, collaborating with peers, rapidly iterating, and uncovering insights. Shakudo becomes the catalyst for data teams to make this critical shift.

In Part 2 of this post, we explore the following specific ways Shakudo facilitates this transformation:

  • Democratizing access to data, with appropriate governance.
  • Moving from one-off pipeline-based data to purpose-built, designed data models.
  • Reducing data debt through documentation and shared dimensions.
  • Empowering agile analysis with self-service tools.
  • Fostering collaborative analytics for better insights.
  • Smoothing onboarding by focusing new users on functionality.

Let's dive in and see how Shakudo helps data teams embrace their true purpose: insight creation. The heavy lifting of data engineering is important, but not the end goal. Shakudo provides the platform for data teams to make analytics and modeling their central focus.

Improving Analytic Productivity 

Many organizations find they get insufficient productivity out of their data infrastructure. It’s understandable, given the challenges of managing the complex, distributed mix of legacy and modern data stacks that make up data operations. The result: Too much time and resources spent on data engineering, and not enough on analytics and insights.

Whatever an organization's mix of data stacks and tools consists of, it requires a big investment in the data engineering plumbing (configuration, connections, credentials, privileges, topology, orchestration) that connects the various components used to build a data infrastructure. A never-ending queue of requests from other teams further burdens data engineering resources. The result: data engineering resources pushed to build quickly assembled, fragile, difficult to maintain one-off data pipelines, leaving little time to prepare, transform, and organize the data for analysis. Documentation suffers, duplicity abounds, and analysts face a confusing array of poorly organized, difficult to understand options for analysts or anyone interested in using data.

The blame doesn't rest with the data engineers — they are doing their best to keep up. What if a tool existed that can help data engineers transition from building data pipelines to enabling the analyst community and other users of data to build their own pipelines without the burden of plumbing and setup? That is what the Shakudo platform provides. It’s a vehicle for pivoting data teams away from the grind of infrastructure towards their true purpose — analysis and discovery.

How does it work? Shakudo allows data engineers to set up environments appropriate for a wide variety of pipeline and analytic tasks. The analytic community then selects the tool they want, completely preconfigured with all connections, credentials, and component compatibility set. Automated DevOps makes integrating with more than [.displaycountclass]100[.displaycountclass] and counting data tools and frameworks a simpler process, ensuring the right tools are available now and in the future, with no friction or fuss.

The benefits are manifold. With Shakudo, the data community can:

  • Build pipelines without the need to install or configure components.
  • Add data quality and transformation steps that make data more usable and comprehensible.
  • Design data models geared towards analysis, sharing, and reuse.
  • Focus more on consistent naming conventions, documentation, and specifying unambiguous sources of key dimensions and facts.
  • Migrate from legacy to modern data stacks using the same platform.

Democratized Data Access

Many organizations want to embrace at least some decentralization of access to their data assets, enabling dispersed teams to become more data savvy. However, building out a decentralized data infrastructure is difficult, often creating a mish-mosh of incompatible components, wildly variant technical competence, and ambiguous, difficult to trust results. A data mess instead of a Data Mesh. Here's where Shakudo shines. The Shakudo platform provides a consistent, secure, governance-compliant set of tools that enables data professionals from anywhere in an organization to get work done, with no delay.

Embracing the Shakudo platform allows data engineers to focus on enabling decentralized organizations, rather than standing in their way, regardless of where users sit.

Plenty of other benefits accrue to an organization trying to decentralize their DataOps with Shakudo:

  • Consistent tool palette for simpler onboarding, training regimes, and easier paths to collaboration.
  • Straightforward processes to better ensure standards, conventions, data sharing, and analytic-oriented data structures.
  • Helps contain "rogue" efforts by teams to build their own data infrastructure — removes the impulse to bypass the backlogs caused when data engineering-centric organizations can't respond quickly enough to data requests.

For many organizations, decentralizing data operations is done for agility, flexibility, and productivity – keeping the data close to those who know it best. However, that same decentralization can become a burden, when organizations deem it appropriate to reorganize or if special projects require coordination across teams. Shakudo has an answer: providing a platform that helps all those who work with data to coalesce around a common set of tools, processes, and governance policies — making reorganizations, interteam transfers, and special projects that much easier to implement.

Purpose-Built Data Structures

Shakudo helps free up analytic resources to design, build, and document analysis-friendly data structures. Dimensional models, precalculated statistics, and understandable naming conventions designed for data sharing all help improve productivity where it matters: more analysis, more model building, more insights. It’s a stark contrast to the gnarly data structures associated with one-off pipelines built to meet narrow analytic needs.

Well documented data models with shared dimensions (like customer and product) are easier to understand and leverage, and create an environment for more consistent results. A boon for organizations pushing for democratizing access to data resources.


Talent Recruiting, Onboarding, and Retention

Shakudo’s adaptable data stack avoids locking organizations into tools with long ramp up times or niche talent pools. The agile platform allows organizations the flexibility to align components with resource preferences — whether via leveraging available skills, training internal teams, or hiring new experts. And if and when new tools become available, or migrations away from old tools becomes appropriate, Shakudo is there to simplify those transitions. Embracing new technologies becomes a viable recruiting strategy for companies using Shakudo, particularly tech that make an immediate difference.

Infrastructure Governance

The curated Shakudo toolbox provides a mechanism for enforcing security, privacy, and regulatory policies. Restricting users to the preconfigured components available on the Shakudo platform puts governance into the knowing hands of the data engineers who set up and install those components, reducing the peril of uncontrolled, sloppy installs and the unintended consequences of lax attention.

Pivot to Insights with Shakudo

Managing complex data infrastructure devours time, leaving insufficient bandwidth for deriving insights. Tactical pipelines proliferate. Data debt accumulates. Analytics suffers.

Shakudo transforms this vicious cycle. The platform pivots data teams from infrastructure drudgery to high-value analytics.

With Shakudo, analysts design reusable models, not one-off pipelines. Self-service access and collaboration foster agile iteration. Data is understandable and documented. Talent onboarding is smoother.

Shakudo also unlocks decentralized analytics without infrastructure sprawl. Governed, democratic data access becomes feasible, and the platform creates a consistent data experience regardless of team location.

In summary, Shakudo enables your evolution to an insight-driven data organization.

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