A Data Platform Leader’s Guide to Choosing the Right Data Observability Solution

Modern data platforms demand more than just basic monitoring. As Databricks adoption grows across the enterprise, platform owners are under pressure to optimize performance, control cloud costs, and deliver reliable data at scale. But with a fragmented vendor landscape and no one-size-fits-all solution, knowing where to start can be a challenge.

This guide simplifies the complex world of data observability and provides a clear, actionable framework for selecting and deploying the right solution for your Databricks environment.

Discover:

• The five core data observability domains every enterprise needs to cover (based on Gartner’s 2024 framework)
• How different solution types—DIY, FinOps, DevOps, native tools, and AI-native platforms—compare
• How the emerging discipline of DataFinOps is more than cost governance
• Which approach best aligns with your goals: cost control, data quality, performance tuning, and scalability
• A phased deployment roadmap for rolling out your selected solution with confidence

If you’re evaluating your data observability options or looking to optimize your Databricks cost and performance, this guide will help you make the best choice for your needs.

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Databricks Health: Sample Data Estate Report

Download a sample report that includes insights into the health of a Databricks data estate:

• Performance insights: See the speedup possible with improved jobs and workflows execution.
• Productivity boost: Uncover top l improvements automatically without ever looking at logs and metrics again.
• Savings projection: View projected annualized savings for clusters and pipelines.
• SLA attainment: Measure potential improvements to data pipeline times.
• Job health: See which jobs are failing most frequently and solve these to improve your Databricks data estate.

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Unravel Data, the first AI-enabled data actionability; and FinOps platform built to address the speed and scale of modern data platforms, today announced it has been named in the 2024 Gartner® Market Guide for Data Observability Tools.

According to Gartner, “By 2026, 50% of enterprises implementing distributed data architectures will have adopted data observability tools to improve visibility over the state of the data landscape, up from less than 20% in 2024.”¹

Gartner also notes that “One of the leading causes for the high demand for data observability is the huge demand for D&A leaders to implement emerging technologies, particularly generative AI (GenAI), in their organization.”¹

Unravel’s Perspective

We believe that the recognition of Unravel as a Representative Vendor underscores our commitment to innovation and excellence in providing comprehensive data observability solutions that empower organizations to drive performance, efficiency, and cost-effectiveness in their data operations.

The Importance of Data Observability

Data observability is crucial in today’s data-driven world. It involves monitoring, tracking, and analyzing data to ensure the health and performance of data systems. Effective data observability helps organizations detect and resolve issues quickly, maintain service-level agreements (SLAs), resource efficiency, and speed time-to-market of AI and other data driven initiatives.

Demand is increasing for robust data observability tools to manage the growing complexity of modern data environments. As data volumes and velocities increase, so does the need for tools that can provide comprehensive visibility and actionable insights. Data observability platforms meet these needs by offering advanced features that support the governance and optimization of performance, costs, and productivity.

Figure 1. Options for data observability and FinOps. Source: Unravel Data.

Go Beyond Data Observability to Actionability^TM

Unravel’s unique approach to data observability enables organizations to take action, automate, and streamline toilsome cloud data analytics platform monitoring, troubleshooting, and cost management. Unravel helps data teams overcome key challenges to accelerate time to value.

Figure 2. The critical features of data observability. Source: Gartner¹.

Unravel’s Comprehensive Data Actionability^TM Platform

Unravel Data’s AI-powered actionability^TM platform is designed to provide deep visibility and enable teams to go beyond observability and take action to improve their modern data stacks, including platforms like Databricks, Snowflake, and BigQuery. AI-powered insights and automation help organizations optimize their data pipelines and infrastructure.

Accelerating Value Across the Organization

Although data observability is typically thought of as being synonymous with data quality, the role of data observability is expanding. As per Gartner, “The increasing complexity in data ecosystems will favor comprehensive data observability tools that can provide additional utilities beyond the monitoring and detection of data issues across platforms.”1 Data observability now includes a variety of new observation categories, including data content, data flow and pipelines, infrastructure and compute, users, usage, and utilization1.

With the introduction of its new AI agents—Unravel FinOps AI Agent, Unravel DataOps AI Agent, and Unravel Data Engineering AI Agent—Unravel enables each role within the organization to simplify and automate recommended actions.

These AI agents are built to tackle specific challenges faced by data teams:

Unravel FinOps AI Agent: This agent provides real-time insights into cloud expenditures, identifying cost-saving opportunities and ensuring budget adherence. It automates financial governance, allowing organizations to manage their data-related costs effectively.

Unravel DataOps AI Agent: By automating routine tasks such as data pipeline monitoring and anomaly detection, this agent frees up human experts to focus on strategic endeavors, enhancing overall productivity and efficiency.

Unravel Data Engineering AI Agent: This agent reduces the burden of mundane tasks for data engineers, enabling them to concentrate on high-value problem-solving. It enhances productivity and ensures precise and reliable data operations.

Figure 3. Unravel’s differentiated value. Source: Unravel Data.

Achieving Data Actionability^TM with Unravel

Unravel’s platform not only observes but also enables teams to take action. By leveraging AI-driven insights, it provides recommendations and automated solutions to optimize data operations. This proactive approach allows organizations to stay ahead of potential issues and continuously improve their data performance and efficiency.

The recent announcement of AI agents exemplifies how Unravel is pushing the boundaries of what data observability can achieve. These agents are designed to lower the barriers to expertise, enabling even under-resourced data teams to operate at peak efficiency.

Get Started Today

Experience the benefits of Unravel’s data actionability^TM platform with a free Health Check Report. This assessment provides insights into the current state of your data operations and quantifies potential improvements to boost performance, cost efficiency, and productivity.

Figure 4. Unravel’s Health Check Report. Source: Unravel Data.

¹Gartner, Market Guide for Data Observability Tools, By Melody Chien, Jason Medd, Lydia Ferguson, Michael Simone, 25 June 2024
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

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Unravel Data, the first AI-enabled data actionability and FinOps platform built to address the speed and scale of modern data platforms, today announced it has been included as a Sample Vendor in the Gartner® Hype Cycle for Container Technology, 2024 in the Augmented FinOps category.

Unravel’s Perspective

How Augmented FinOps Helps

Augmented FinOps empowers organizations by automating and enhancing financial operations through AI and automation. This innovative approach provides real-time insights into cloud spending, identifies cost-saving opportunities, and ensures budget adherence. By leveraging domain-specific knowledge and intelligent automation, Augmented FinOps reduces manual workload, improves financial accuracy, and optimizes resource allocation. Ultimately, it drives efficiency, enabling businesses to focus on strategic growth while ensuring financial health and governance.

Introducing Unravel’s New AI Agents

Unravel recently announced the three groundbreaking new AI agents: the Unravel FinOps AI Agent, the Unravel DataOps AI Agent, and the Unravel Data Engineering AI Agent. These AI agents are designed to transform how data teams manage and optimize their operations. The Unravel FinOps AI Agent helps automate financial governance, providing near real-time insights into cloud expenditures with showback and chargeback reports, identifying cost-saving opportunities and enabling action. The Unravel DataOps AI Agent streamlines data pipeline monitoring and anomaly detection, and troubleshooting, freeing up human experts for more strategic tasks. Meanwhile, the Unravel Data Engineering AI Agent enhances productivity by automating routine tasks, allowing data engineers to focus on high-value problem-solving. Together, these AI agents empower organizations to achieve greater efficiency, accuracy, and innovation in their data operations, driving transformative business outcomes.

Three Keys to Optimizing Containers for Your Modern Data Stack

In today’s fast-paced digital landscape, optimizing containers is crucial for the efficiency and scalability of your modern data stack. Here are three key strategies to ensure your containerized environments are performing at their best:

1. Implement FinOps for Containers:

FinOps principles like monitoring, optimization, automation, and cost allocation can be applied to container workloads to enhance their efficiency, scalability, and cost-effectiveness. Advanced observability tools, integrated with AI-driven insights, can proactively identify potential problems and suggest optimizations, keeping your data stack running efficiently.

2. Right-Size Your Containers:

Properly sizing your containers is essential to prevent resource wastage and ensure optimal performance. Over-provisioning can lead to unnecessary costs, while under-provisioning can cause performance bottlenecks. Utilize AI-powered tools and automation that provide real-time monitoring and analytics to understand your workloads’ demands and adjust resources accordingly. This dynamic approach helps maintain a balance between cost and performance, ensuring your applications run smoothly without incurring excessive expenses.

3. Automate Management and Scaling:

Automation is a game-changer in container management, allowing for seamless scaling and resource allocation based on real-time demands. Employ automation tools that can handle tasks such as load balancing, resource provisioning, and fault tolerance. Kubernetes, for example, offers powerful automation capabilities that can dynamically manage container orchestration, ensuring your data stack can scale efficiently as your workloads grow. By automating these processes, you reduce the risk of human error, increase operational efficiency, and ensure that your infrastructure can adapt to changing demands without manual intervention.

Optimizing containers using a FinOps approach enables teams to right-size, automate, and scale not only enhances performance and cost-efficiency but also ensures your modern data stack is resilient, scalable, and ready to meet the demands of today’s data-driven world.

Next Steps

Ready to optimize your data operations? Discover the transformative impact of Unravel’s new AI agents. Request a free health check to see how your organization can improve performance, efficiency, and cost management. Start your journey towards smarter, more actionable data insights today.

Gartner, Hype Cycle for Container Technology, By Dennis Smith, 20 June 2024
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

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Snowflake Health: Sample Data Estate Report

Download a sample report that includes insights into the health of a Snowflake data estate:

• Performance insights: See the speedup possible with improved warehouse utilization.
• Productivity boost: Uncover top operational improvements with ease.
• Savings projection: View projected annualized savings for warehouses and queries.
• SLA attainment: Measure potential improvements to data pipeline times.
• Query health: See which queries are failing most frequently and solve these to improve your Snowflake data estate.

The post Discover Your Snowflake Health: Sample Data Estate Report appeared first on Unravel.

Palo Alto, CA – Apr. 24, 2024 – Unravel Data is proud to announce A.P. Moller – Maersk, a global leader in container logistics, is participating at the upcoming Gartner Data & Analytics Summit. Scheduled from May 13-15, 2024, at ExCeL London, this summit is renowned for gathering visionaries and innovators in the realm of data and analytics.

Peter Rees, Lead Architect at Maersk, together with Mark Sear, Director of Insight Analytics, Data, and Integration at Maersk, will be leading a must-attend session titled “Unravel Data: How to stop burning money (or at least slow the burn).” This session is meticulously designed to address the escalating concern of unpredictable growth in data analytics costs which can significantly hinder the progression of data-driven innovation.

Session Overview:

Data analytics costs are spiraling, and businesses are searching for methodologies to efficiently manage and optimize these expenses without compromising on innovation or operational agility. Maersk, leveraging Unravel Data’s cutting-edge solutions, has pioneered a cost-optimization framework that not only streamlines development and data delivery processes but also aligns with the enterprise’s mission to deliver a more connected, agile, and sustainable future for global logistics.

During the session, attendees will gain exclusive insights into how Maersk has successfully harnessed the power of Unravel Data within its infrastructure to ensure the business remains at the forefront of cost efficiency while bolstering its data-driven decision-making capabilities.

Speaker Highlights:

• Peter Rees, as Maersk’s Lead Architect specializing in Enterprise Data & AI/ML, brings a wealth of knowledge in data mesh and event-driven architectures. His extensive track record in AI strategies and analytics, complemented by an innovative mindset, positions him as a cornerstone in the conversation on bridging data technology with business value.
• Mark Sear elevates the discourse with his profound expertise in digital transformation and business intelligence as Maersk’s Director of Insight Analytics, Data, and Integration. Mark’s academic and professional achievements underscore his commitment to leveraging data for actionable insights, thus fostering strategic business growth and operational efficiencies.

Event Details:

• What: Gartner Data & Analytics Summit
• When: May 13-15, 2024
• Where: ExCeL London, UK
• Session Title: Unravel Data: How to stop burning money (or at least slow the burn)

Unravel Data invites all attendees who are looking to navigate the challenges of data analytics cost growth to join this session. It promises to be an enlightening exploration of practical solutions, real-world applications, and visionary strategies for any organization aiming to optimize their data-driven initiatives and investments.

About A.P. Moller – Maersk:
A.P. Moller – Maersk is an integrated container logistics company working to connect and simplify its customers’ supply chains. As a global leader in shipping services, the company operates in 130 countries and employs over 80,000 people. For further information, visit www.maersk.com.

About Unravel Data:
Unravel’s automated, AI-powered data observability + FinOps provides 360° visibility to allocate costs with granular precision, accurately predict spend, run 50% more workloads at the same budget, launch new apps 3X faster, and reliably hit greater than 99% of SLAs. For further information, visit www.unraveldata.com.

Media Contact:
Keith Alsheimer
CMO, Unravel Data
hello@unraveldata.com

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Harnessing Data Observability for AI-Driven Innovation

Organizations are now embarking on a journey to harness AI for significant business advancements, from new revenue streams to productivity gains. However, the complexity of delivering AI-powered software efficiently and reliably remains a challenge. With AI investments expected to surge beyond $520 billion by 2027, this brief underscores the necessity for a robust intelligence architecture, scalable digital operations, and specialized skills. Learn how AI-driven data observability can be leveraged as a strategic asset for businesses aiming to lead in innovation and operational excellence.

Get a copy of the IDC Analyst Brief by Research Director Nancy Gohring.

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Business leaders from every industry now find themselves under the gun to somehow, someway leverage AI into an actual product that companies (and individuals) can use. Yet, an estimated 70%-85% of artificial intelligence (AI) and machine learning (ML) projects fail.

In our thought-leadership white paper Winning the AI Innovation Race: How AI Helps Optimize Speed to Market and Cost Inefficiencies of AI Innovation, you will learn:

• Top pitfalls that impede speed and ROI for running AI and data pipelines in the cloud

• How the answers to addressing these impediments can be found at the code level

• How AI is paramount for optimization of cloud data workloads

• How Unravel helps

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Check out the 2023 Intellyx Analyst Guide for Unravel, Demystifying Data Observability, for an independent discussion on the specific requirements and bottlenecks of data-dependent applications/pipelines that are addressed by data observability.

Discover:

• Why DataOps needs its own observability
• How DevOps and DataOps are similar–and how they’re very different
• How the emerging discipline of DataFinOps is more than cost governance
• Unique considerations of DataFinOps
• DataOps resiliency and tracking down toxic workloads

The post Demystifying Data Observability appeared first on Unravel.

Check out the latest white paper from Eckerson Group: Governing Cost with FinOps for Cloud Analytics: Program Elements, Use Cases, and Principles by VP of Research Kevin Petrie.

• Discover how to turn cloud usage-based pricing to your advantage.
• Learn how data observability tools can help you pay for only what you use, and only use what you need.
• Read about FinOps uses cases, including business, data, and IT.
• Read how cross-functional teams govern cloud costs as they forecast, monitor, and account for resources.
• Explore the FinOps lifecycle, including design, operation, and optimization.

Download this actionable white paper today and discover how a well-implemented FinOps program can drive measurable, achievable ROI for cloud-based analytics projects.

Get your free copy of the white paper.

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Check out Sanjeev Mohan’s, Principal, SanjMo & Former Gartner Research VP, Big Data and Advanced Analytics, chapter on “Data observability’s newest frontiers: DataFinOps and DataBizOps” in the book, Data Observability, The Reality.

What you’ll learn from the former VP of Research at Gartner:

• DataFinOps defined
• Why you need DataFinOps
• The challenges of DataFinOps
• DataFinOps case studies & more!

Don’t miss out on your chance to read this chapter and gain valuable insights from a top industry leader.

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Thrilled to announce that Unravel is contributing a chapter in Ravit Jain’s new ebook, Data Observability, The Reality.

What you’ll learn from reading this ebook:

• What data observability is and why it’s important
• Identify key components of an observability framework
• Learn how to design and implement a data observability strategy
• Explore real-world use cases and best practices for data observability
• Discover tools and techniques for monitoring and troubleshooting data pipelines

Don’t miss out on your chance to read our chapter, “Automation and AI Are a Must for Data Observability,” and gain valuable insights from top industry leaders such as Sanjeev Mohan and others.

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Thank you for your interest in the Data Teams Summit 2023 Survey Infographic.

Download the full pdf.

Take a look at last year’s survey results here.

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Analyst Deep Dive into Unravel

This Eckerson Group report gives you a good understanding of how the Unravel platform addresses multiple categories of data observability—application/pipeline performance, cluster/platform performance, data quality, and, most significant, FinOps cost governance—with automation and AI-driven recommendations.

Eckerson Group, a leading global research, consulting, and advisory firm that focuses solely on data and analytics, profiles various elements of Unravel Data:

• Target customers and use cases
• Product functionality
• Differentiators
• Product architecture
• Pricing

You’ll walk away with a clear picture of what Unravel does, how it does it, and why its features and capabilities are crucial for today’s DataOps and FinOps teams.

Eckerson Group profiles provide independent and objective research on products they believe offer exceptional value to customers.

The post Eckerson Report: Data Observability for Modern Digital Enterprises appeared first on Unravel.

Thank you for your interest in the DataOps Unleashed 2022 Survey Infographic.

You can download your copy here.

Looking for the 2023 results. Find the infographic here.

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Thank you for your interest in the 451 Research Report, Living on the edge: A primer on hybrid cloud and edge infrastructure.

You can download it here.

451 Research: Living on the edge: A primer on hybrid cloud and edge infrastructure
Published Date: October 11, 2021

Introduction
Without the internet, the cloud is nothing. But few of us really understand what is inside the internet. What is the so-called ‘edge’ of the internet, and why does it matter? And how does cloud play into the edge story? This primer seeks to explain these issues to a non-tech audience.

Get the 451 Take. Download Report.

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Thank you for your interest in the 451 Research Report, Data Science and analytics in the cloud set to grow three times faster than on-premises.

You can download it here.

451 Research: Data Science and analytics in the cloud set to grow three times faster than on-premises
Published: September, 28 2021

Introduction
Data science and analytics, as well as the data abstraction and acceleration offerings that underpin them, represented a $29bn market in 2020, according to 451 Research’s Data, AI & Analytics Market Monitor: Data Science & Analytics. Moreover, this market is growing thanks to the critical role of data, AI and analytics in speeding up enterprise data-driven decision-making for faster time to insight. Cloud services, in particular, are exhibiting strong growth – a trend that has been underway for some time and has been accelerated by the COVID-19 pandemic.

Get the 451 Take. Download Report.

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Read The Unravel Guide to DataOps

The Unravel Guide to DataOps gives you the information and tools you need to sharpen your DataOps practices and increase data application performance, reduce costs, and remove operational headaches. The ten-step DataOps lifecycle shows you everything you need to do to create data products that run and run.

The Guide introduces Unravel Data software as a powerful platform, helping you to create a robust and effective DataOps culture within your organization. Three customer use cases show you the power of DataOps, and Unravel, in the hands of your data teams. As one Unravel customer says in the Guide, “I’m sleeping a lot easier than I did a year ago.”

Download the Guide to help you as you master the current state, and future possibilities, of DataOps adoption in organizations worldwide.

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Thank you for your interest in the Unravel Data2021 Infographic.

You can download it here.

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As your organization begins planning and budgeting for 2021 initiatives, it’s time to take a critical look at your cloud migration strategy. If you’re planning to move your on-premises big data workloads to the cloud this year, you’re undoubtedly faced with a number of questions and challenges:

• Which workloads are best suited for the cloud?
• How much will each workload cost to run?
• How do you manage workloads for optimal performance, while keeping costs down?

Gartner Cloud Migration Report

Neglecting careful workload planning and controls prior to cloud migration can lead to unforeseen cost spikes. That’s why we encourage you to read Gartner’s new report that cites serious gaps in how companies move to the cloud: “Mind the Gaps in DBMS Cloud Migration to Avoid Cost and Performance Issues.”

Gartner’s timely report provides invaluable information for any enterprise with substantial database spending, whether on-premises, in the cloud, or migrating to the cloud. Organizations typically move to the cloud to save money, cutting costs by an average of 21% according to the report. However, Gartner finds that migrations are often more expensive and disruptive than initially planned because organizations neglect three crucial steps:

• Price/performance comparison. They fail to assess the price and performance of their apps, both on-premises and after moving to the cloud.
• Apps conversion assessment. They don’t assess the cost of converting apps to run effectively in the cloud, then get surprised by failed jobs and high costs.
• Ops conversion assessment. DataOps tasks change greatly across environments, and organizations don’t maximize their gains from the move.

When organizations do not to take these important steps, they typically fail to complete the migration on-time, overspend against their established cloud operational budgets, and miss critical optimization opportunities available in the cloud.

Remove the Risk of Cloud Migration With Unravel Data

Unravel Data can help you fill in the gaps cited in the Gartner report, providing full-stack observability and AI-powered recommendations to drive more reliable performance on Azure, AWS, Google Cloud Platform or your in own data center. By simplifying, operationalizing, and automating performance improvements, applications are more reliable, and costs are lower. Your team and your workflows will be more efficient and productive, so you can focus your resources on your larger vision.

To learn more – including information about our Cloud Migration Acceleration Programs – contact us today. And make sure to download your copy of the Gartner report. Or start by reading our two-page executive summary.

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Get DataOps Deep Dive

Nowadays, companies of all types spend heavily on compute power across a wide range of data technologies, but frequently don’t place enough emphasis pre-planning where that money will be allocated. This is particularly true for organizations that are moving to the cloud.

As uncovered in the Eckerson report DataOps: Deep Dive, providing insights into efficiency, from the level of KPIs down to lines of code, helps organizations understand and improve the ROI of their data initiatives. At the same time, the integration of AI to automate efficiency improvements saves developers valuable time in the DataOps lifecycle. And, this is an area where Unravel excels.

As described in the report, “The Unravel Data Operations Platform is, in many respects, a next-gen application performance monitor tailored to the unique challenges of development in the modern big data context. Unravel works to alleviate this complexity by providing full-stack visibility across the data ecosystem.”

Download the report to learn how to add advanced monitoring capabilities to your DataOps strategy whether your high-volume data environment is on-premise, hybrid, or in the cloud.

The Unravel Data Operations Platform is ideal for customers that want to:

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Thank you for your interest in the Unravel AWS Cloud Migration eBook.

You can download it here.

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Solving a problem programmatically often involves grouping data items together so they can be conveniently operated on or copied as a single unit – the items are collected in a data structure. Many different data structures have been designed over the past decades, some store individual items like phone numbers, others store more complex objects like name/phone number pairs. Each has strengths and weaknesses and is more or less suitable for a specific use case. In this article, I will describe and attempt to classify some of the most popular data structures and their actual implementations on three different abstraction levels starting from a Platonic ideal and ending with actual code that is benchmarked:

• Theoretical level: Data structures/collection types are described irrespective of any concrete implementation and the asymptotic behavior of their core operations are listed.
• Implementation level: It will be shown how the container classes of a specific programming language relate to the data structures introduced at the previous level – e.g., despite their name similarity, Java’s Vector is different from Scala’s or Clojure’s Vector implementation. In addition, asymptotic complexities of core operations will be provided per implementing class.
• Empirical level: Two aspects of the efficiency of data structures will be measured: The memory footprints of the container classes will be determined under different configurations. The runtime performance of operations will be measured which will show to what extent asymptotic advantages manifest themselves in concrete scenarios and what the relative performances of asymptotically equal structures are.

Theoretical Level

Before providing actual speed and space measurement results in the third section, execution time and space can be described in an abstract way as a function of the number of items that a data structure might store. This is traditionally done via Big O notation and the following abbreviations are used throughout the tables:

• C is constant time, O(1)
• aC is amortized constant time
• eC is effective constant time
• Log is logarithmic time, O(log n)
• L is linear time, O(n)

The green, yellow or red background colors in the table cells will indicate how “good” the time complexity of a particular data structure/operation combination is relative to the other combinations.

Click for Full Size

The first five entries of Table 1 are linear data structures: They have a linear ordering and can only be traversed in one way. By contrast, Trees can be traversed in different ways, they consist of hierarchically linked data items that each have a single parent except for the root item. Trees can also be classified as connected graphs without cycles; a data item (= node or vertex) can be connected to more than two other items in a graph.

Data structures provide many operations for manipulating their elements. The most important ones are the following four core operations which are included above and studied throughout this article:

• Access: Read an element located at a certain position
• Search: Search for a certain element in the whole structure
• Insertion: Add an element to the structure
• Deletion: Remove a certain element

Table 1 includes two probabilistic data structures, Bloom Filter and Skip List.

Implementation Level – Java & Scala Collections Framework

The following table classifies almost all members of both the official Java Collection and Scala Collection libraries in addition to a number of relevant classes like Array or String that are not canonical members. The actual class names are placed in the second column, a name that starts with im. or m. refers to a Scala class, other prefixes refer to Java classes. The fully qualified class names are shortened by using the following abbreviations:

• u. stands for the package java.util
• c. stands for the package java.util.concurrent
• lang. stands for the package java.lang
• m. stands for the package scala.collection.mutable
• im. stands for the package scala.collection.immutable

The actual method names and logic of the four core operations (Access, Search, Addition and Deletion) are dependent on a concrete implementation. In the table below, these method names are printed right before the asymptotic times in italic (they will also be used in the core operation benchmarks later). For example: Row number eleven describes the implementation u.ArrayList (second column) which refers to the Java collection class java.util.ArrayList. In order to access an item in a particular position (fourth column, Random Access), the method get can be called on an object of the ArrayList class with an integer argument that indicates the position. A particular element can be searched for with the method indexOf and an item can be added or deleted via add or remove. Scala’s closest equivalent is the class scala.collection.mutable.ArrayBuffer which is described two rows below ArrayList: To retrieve the element in the third position from an ArrayBuffer, Scala’s apply method can be used which allows an object to be used in function notation, Ss we would write val thirdElement = bufferObject(2). Searching for an item can be done via find and appending or removing an element from an ArrayBuffer is possible by calling the methods += and -= respectively.

Click for Full Size

Subclass and wrapping relationships between two classes are represented via <e) and <w). For example, the class java.util.Stack extends java.util.Vector and the Scala class scala.collection.mutable.StringBuilder wraps the Java class java.lang.StringBuilder in order to provide idiomatic functions and additional operations.

General features of Java & Scala structures

Several collection properties are not explicitly represented in the table above since they either apply to almost all elements or a simple rule exists:

Almost all data structures that store key/value pairs have the characters Map as part of their class name in the second column. The sole exception to this naming convention is java.util.Hashtable which is a retrofitted legacy class born before Java 2 that also stores key/value pairs.

Almost all Java Collections are mutable: They can be destroyed, elements can be removed from or added to them and their data values can be modified in-place, mutable structures can therefore loose their original/previous state. By contrast, Scala provides a dedicated immutable package (scala.collection.immutable) whose members, in contrast to scala.collection.mutable and the Java collections, cannot be changed in-place. All members of this immutable package are also persistent: Modifications will produce an updated version via structural sharing and/or path copying while also preserving the original version. Examples of immutable but non-persistent data structures from third party providers are mentioned below.

Mutability can lead to problems when concurrency comes into play. Most classes in Table 2 that do not have the prefix c. (abbreviating the package java.util.concurrent) are unsynchronized. In fact, one of the design decision made in the Java Collections Framework was to not synchronize most members of the java.util package since single-threaded or read-only uses of data structures are pervasive. In case synchronization for these classes is required, java.util.Collections provides a cascade of synchronized* methods that accept a given collection and return a synchronized, thread-safe version.

Due to the nature of immutability, the (always unsynchronized) immutable structures in Table 2 are thread-safe.

All entries in Table 2 are eager except for scala.collection.immutable.Stream which is a lazy list that only computes elements that are accessed.

Java supports the eight primitive data types byte, short, int, long, float, double, boolean and char. Things are a bit more complicated with Scala but the same effectively also applies there at the bytecode level. Both languages provide primitive and object arrays but the Java and Scala Collection libraries are object collections which always store object references: When primitives like 3 or 2.3F are inserted, the values get autoboxed so the respective collections will hold a reference to numeric objects (a wrapper class like java.lang.Integer) and not the primitive values themselves:

int[] javaArrayPrimitive = new int[]{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
Integer[] javaArrayObject = new Integer[]{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
// javaArrayPrimitive occupies just 64 Bytes, javaArrayObject 240 Bytes

List<Integer> javaList1 = new ArrayList<>(11); // initial capacity of 11
List<Integer> javaList2 = new ArrayList<>(11);
for (int i : javaArrayPrimitive)
javaList1.add(i);
for (int i : javaArrayObject)
javaList2.add(i);
// javaList1 is 264 bytes in size now as is javaList2

Similar results for Scala:

val scalaArrayPrimitive = Array[Int](1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
val scalaArrayObject = scalaArrayPrimitive.map(new java.lang.Integer(_))
// scalaArrayPrimitive occupies just 64 Bytes, scalaArrayObject 240 Bytes

val scalaBuffer1 = scalaArrayPrimitive.toBuffer
val scalaBuffer2 = scalaArrayObject.toBuffer
// scalaBuffer1 is 264 bytes in size now as is scalaBuffer2

Several third party libraries provide primitive collection support on the JVM allowing the 8 primitives mentioned above to be directly stored in data structures. This can have a big impact on the memory footprint – the creators of Apache Spark recommend in their official tuning guide to

Design your data structures to prefer arrays of objects, and primitive types, instead of the standard Java or Scala collection classes (e.g. HashMap). The fastutil library provides convenient collection classes for primitive types that are compatible with the Java standard library.

We will see below whether FastUtil is really the most suitable alternative.

Empirical Level

Hardly any concrete memory sizes and runtime numbers have been mentioned so far, these two measurements are in fact very different: Estimating memory usage is a deterministic task compared to runtime performance since the latter might be influenced by several non-deterministic factors, especially when operations run on an adaptive virtual machine that performans online optimizations.

Memory measurements for JVM objects

Determining the memory footprint of a complex object is far from trivial since JVM languages don’t provide a direct API for that purpose. Apache Spark has an internal function for this purpose that implements the suggestions of an older JavaWorld article. I ported the code and modified it a bit here so this memory measuring functionality can be conveniently used outside of Spark:

val objectSize = JvmSizeEstimator.estimate(new Object())
println(objectSize) // will print 16 since one flat object instance occupies 16 bytes

Measurements for the most important classes from Table 2 with different element types and element sizes are shown below. The number of elements will be 0, 1, 4, 16, 64, 100, 256, 1024, 4096, 10000, 16192, 65536, 100000, 262144, 1048576, 4194304, 10000000, 33554432 and 50000000 in all configurations. For data structures that store individual elements, the two element types are int and String. For structures operating with key/value pairs, the combinations int/int and float/String will be used. The raw sizes of these element types are 4 bytes in the case of an individual int or float (16 bytes in boxed form) and, since all Strings used here will be 8 characters long, 56 bytes per String object.

The same package abbreviations as in Table 2 above will be used for the Java/Scala classes under measurement. In addition, some classes from the following 3rd party libraries are also used in their latest edition at the time of writing:

• HPPC (0.8.1)
• Koloboke (1.0.0)
• fastutil (8.2.1)
• Eclipse Collections (9.2.0)

Concerning the environment, jdk1.8.0_171.jdk on MacOS High Sierra 10.13 was used. The JVM flag +UseCompressedOops can affect object memory sizes and was enabled here, it is enabled by default in Java 8.

Measurements of single element structures

Below are the measurement results for the various combinations, every cell contains the object size in bytes for the particular data structure in the corresponding row filled with the number of elements indicated in the column. Some mutable classes provide the option to specify an initial capacity at construction time which can sometimes lead to a smaller overall object footprint after the structure is filled up. I included an additional + capacity row in cases where data structure in the previous row provides such an option and a difference could be measured.

Java/Scala structures storing integers:

Java/Scala structures storing strings:

3rd party structures storing integers:

3rd party structures storing strings:

Measurements for key/value structures:

For some mutable key/value structures like Java’s HashMap, a load factor that determines when to rehash can be specified in addition to an initial capacity. Similar to the logic in the previous tables, a row with + capacity will indicate that the data structure from the previous row was initialized using a capacity.

Java/Scala structures storing integer/integer pairs:

Java/Scala structures storing strings/float pairs:

3rd party structures storing integer/integer pairs:

3rd party structures storing strings/float pairs:

The post Data Structure Zoo appeared first on Unravel.

Organizations today are building strategic applications using a wealth of internal and external data. Unfortunately, data-driven applications that combine customer data from multiple business channels can fail for many reasons. Identifying the cause and finding a fix is both challenging and time-consuming. With this practical ebook, DevOps personnel and enterprise architects will learn the processes and tools required to build and manage modern data pipelines.

Ted Malaska, Director of Enterprise Architecture at Capital One, examines the rise of modern data applications and guides you through a complete data operations framework. You’ll learn the importance of testing and monitoring when planning, building, automating, and managing robust data pipelines in the cloud, on premises, or in a hybrid configuration.

Plan, migrate, and operate modern data stack workloads and data pipelines using cloud-based and hybrid deployment models

The post Rebuilding Reliable Modern Data Pipelines Using AI and DataOps appeared first on Unravel.

In the past five years, a shift in Cloud Vendor offerings has fundamentally changed how companies buy, deploy and run big data systems. Cloud Vendors have absorbed more back-end data storage and transformation technologies into their core offerings and are now highlighting their data pipeline, analysis, and modeling tools. This is great news for companies deploying, migrating, or upgrading big data systems. Companies can now focus on generating value from data and Machine Learning (ML), rather than building teams to support hardware, infrastructure, and application deployment/monitoring.

The following chart shows how more and more of the cloud platform stack is becoming the responsibility of the Cloud Vendors (shown in blue). The new value for companies working with big data is the maturation of Cloud Vendor Function as a Service (FaaS), also known as serverless, and Software as a Service (SaaS) offerings. For FaaS (serverless) the Cloud Vendor manages the applications and users focus on data and functions/features. With SaaS, features and data management become the Cloud Vendor’s responsibility. Google Analytics, Workday, and Marketo are examples of SaaS offerings.As the technology gets easier to deploy, and the Cloud Vendor data services mature, it becomes much easier to build data-centric applications and provide data and tools to the enterprise. This is good news: companies looking to migrate from on-premise systems to the cloud are no longer required to purchase directly or manage hardware, storage, networking, virtualization, applications, and databases. In addition, this changes the operational focus for a big data systems from infrastructure and application management (DevOps) to pipeline optimization and data governance (DataOps). The following table shows the different roles required to build and run Cloud Vendor-based big data systems.

This article is aimed at helping big data systems leaders moving from on-premise or native IaaS (compute, storage, and networking) deployments understand the current Cloud Vendor offerings. Those readers new to big data, or Cloud Vendor services, will get a high-level understanding of big data system architecture, components, and offerings. To facilitate discussion we provide an end-to-end taxonomy for big data systems and show how the three leading Cloud Vendors (AWS, Azure and GCP) align to the model:

• Amazon Web Services (AWS)
• Microsoft Azure (Azure)
• Google Cloud Platform (GCP)

APPLYING A COMMON TAXONOMY

Understanding Cloud Vendor offerings and big data systems can be very confusing. The same service may have multiple names across Cloud Vendors and, to complicate things even more, each Cloud Vendor has multiple services that provide similar functionality. However, the Cloud Vendors big data offerings align to a common architecture and set of workflows.

Each big data offering is set up to receive high volumes of data to be stored and processed for real-time and batch analytics as well as more complex ML/AI modeling. In order to provide clarity amidst the chaos, we provide a two-level taxonomy. The first-level includes five stages that sit between data sources and data consumers: CAPTURE, STORE, TRANSFORM, PUBLISH, and CONSUME. The second-level taxonomy includes multiple service offerings for each stage to provide a consistent language for aligning Cloud Vendor solutions.The following sections provide details for each stage and the related service offerings.

CAPTURE

Persistent and resilient data CAPTURE is the first step in any big data system. Cloud Vendors and the community also describe data CAPTURE as ingest, extract, collect, or more generally as data movement. Data CAPTURE includes ingestion of both batch and streaming data. Streaming event data becomes more valuable by being blended with transactional data from internal business applications like SAP, Siebel, Salesforce, and Marketo. Business application data usually resides within a proprietary data model and needs to be brought into the big data system as changes/transactions occur.

Cloud Vendors provide many tools for bringing large batches of data into their platforms. This includes database migration/replication, processing of transactional changes, and physical transfer devices when data volumes are too big to send efficiently over the internet. Batch data transfer is common for moving on-premise data sources and bringing in data from internal business applications, both SaaS and on-premise. Batch data can be run once as part of an application migration or in near real-time as transactional updates are made in business systems.

The focus of many big data Pipeline implementations is the capture of real-time data streaming in as an application clickstream, product usage events, application logs, and IoT sensor events. To properly capture streaming data requires configuration on the edge device or application. For, example, collecting clickstream from a mobile or web application requires events to be instrumented and sent back to an endpoint listening for the events. This is similar with IoT devices, which may also perform some data processing on the edge device prior to sending it back to an end point.

STORE

For big data systems the STORE stage focuses on the concept of a data lake, a single location where structured, semi-structured, unstructured data and objects are stored together. The data lake is also a place to store the output from extract, transform, load (ETL) and ML pipelines running in the TRANSFORM stage. Vendors focus on scalability and resilience over read/write performance. To increase data access and analytics performance, data should be highly aggregated in the data lake or organized and placed into higher performance data warehouses, massively parallel processing (MPP) databases, or key-value stores as described in the PUBLISH stage. In addition, some data streams have such high event volume, or the data are only relevant at the time of capture, that the data stream may be processed without ever entering the data lake.

Cloud Vendors have recently put more focus on the concept of the data lake, by adding functionality to their object stores and creating a much tighter integration with TRANSFORM and CONSUME service offerings. For example, Azure created Data Lake Storage on top of the existing Object Store with additional services for end to end analytics pipelines. Also, AWS now provides Data Lake Formation to make it easier to set up a data lake on their core object store S3.

TRANSFORM

The heart of any big data implementation is the ability to create data pipelines in order to clean, prepare, and TRANSFORM complex multi-modal data into valuable information. Data TRANSFORM is also described as preparing, massaging, processing, organizing, and analyzing among other things. The TRANSFORM stage is where value is created and, as a result, Cloud Vendors, start-ups, and traditional database and ETL vendors provide many tools. The TRANSFORM stage has three main data pipeline offerings including Batch Processing, Machine Learning, and Stream Processing. In addition, we include the Orchestration offering because complex data pipelines require tools to stage, schedule, and monitor deployments.

Batch TRANSFORM uses traditional extract, TRANSFORM, and load techniques that have been around for decades and are the purview of traditional RDBMS and ETL vendors. However, with the increase in data volumes and velocity, TRANSFORM now commonly comes after extraction and loading into the data lake. This is referred to as extract, load, and transform or ELT. Batch TRANSFORM uses Apache Spark or Hadoop to distribute compute across multiple nodes to process and aggregate large volumes of data.

ML/AI uses many of the same Batch Processing tools and techniques for data preparation and for the development and training of predictive models. Machine Learning also takes advantage numerous libraries and packages to help optimize data science workflows and provide pre-built algorithms.
big data systems also provide tools to query continuous data streams in near real-time. Some data has immediate value that would be lost waiting for a batch process to run. For example, predictive models for fraud detection or alerts based on data from an IoT sensor. In addition, streaming data is commonly processed, and portions are loaded into a data lake.

Cloud Vendor offerings for TRANSFORM are evolving quickly and it can be difficult to understand which tools to use. All three Cloud Vendors have versions of Spark/Hadoop that scale on their IaaS compute nodes. However, all three now provide serverless offerings that make it much simpler to build and deploy data pipelines for batch, ML and streaming workflows. For example, AWS EMR, GCP Cloud Data Proc, and Azure Databricks provide Spark/Hadoop that scale by adding additional compute resources. However, they also offer the serverless AWS Glue, GCP Data Flow, and Azure Data Factory which abstract away the need to manage compute nodes and orchestration tools. In addition, they now all provide end-to-end tools to build, train, and deploy machine learning models quickly. This includes data preparation, algorithm development, model training algorithm, and deployment tuning and optimization.

PUBLISH

Once through the data CAPTURE and TRANSFORM stages it is necessary to PUBLISH the output from batch, ML, or streaming pipelines for users and applications to CONSUME. PUBLISH is also described as deliver or serve, and comes in the form of Data Warehouses, Data Catalogs, or Real-Time Stores.

Data Warehouse solutions are abundant in the market, and the choice depends on the data scale and complexity as well as performance requirements. Serverless relational databases are a common choice for Business Intelligence applications and for publishing data for other systems to consume. They provide scale and performance and, most of all, SQL-based access to the prepared data. Cloud Vendor examples include AWS Redshift, Google BigQuery, and Azure SQL Data Warehouse. These work great for moderately sized and relatively simple data structures. For higher performance and complex relational data models, massively parallel processing (MPP) databases store large volumes of data in-memory and can be blazing fast, but often at a steep price.

As the tools for TRANSFORM and CONSUME become easier to use, data analyses, models, and metrics proliferate. It becomes harder to find valuable, governed, and standardized metrics in the mass of derived tables and analyses. A well-managed and up-to-date data catalog is necessary for both technical and non-technical users to manage and explore published tables and metrics. Cloud Vendor Data Catalog offerings are still relatively immature. Many companies build their own or use third party catalogs like Alation or Waterline. More technical users including data engineers and data scientists explore both raw and transformed data directly in the data lake. For these users the data catalog, or metastore, is the key for various compute options to understand where data is and how it is structured.

Many streaming applications require a Real-Time Store to meet millisecond response times. Hundreds of optimized data stores exist in the market. As with Data Warehouse solutions, picking a Real-Time Store depends on the type and complexity of the application and data. Cloud Vendors examples include AWS DynamoDB, Google Bigtable, and Azure Cosmos DB providing wide-column or key-value data stores. These are applied as high performance in-process databases and improve the performance of Data processing and analytics workloads.

CONSUME

The value of any big data system comes together in the hands of technical and non-technical users, and in the hands of customers using data-centric applications and products. Vendors also refer to CONSUME as use, harness, explore, model, infuse, and sandbox. We discuss three CONSUME models: Advanced Analytics, Business Intelligence (BI), and Real-Time APIs.

Aggregated data does not always allow for deeper data exploration and understanding. So, advanced analytics users CONSUME both raw and processed data either directly from the data lake or from a Data Warehouse. Advanced analytics users use similar tools from the TRANSFORM stage including Spark- and Hadoop-based distributed compute. In addition, notebook technologies are a popular tool that allow data engineers and data scientists to create documents containing live code, equations, visualizations and text. Notebooks allow users to code in a variety of languages, run packages, and share the results. All three Cloud Vendors offer notebook solutions, most based on the popular open source Jupyter project.

BI tools have been in the market for a couple of decades and are now being optimized to work with larger data sets, new types of compute, and directly in the cloud. Each of the three cloud vendors now provide a BI tool optimized to work with their stack. These include AWS Quicksight, GCP Data Studio, and Microsoft Power BI. However, several more mature BI tools exist in the market that work with data from most vendors. BI tools are optimized to work with published data and usage improves greatly with an up-to-date data catalog and some standardization of tables and metrics.

Applications, products, and services also CONSUME raw and transformed data through APIs built on the Real-Time Store or predictive ML models. The same Cloud Vendor ML offerings used to explore and build models also provides Real-Time APIs for alerting, analysis, and personalization. Example use cases include fraud detection, system/sensor alerting, user classification, and product personalization.

CLOUD VENDOR OFFERINGS

AWS, GCP and AZURE have very complex cloud offerings based on their core networking, storage and compute. In addition, they provide vertical offerings for many markets, and within the big data systems and ML/AI verticals they each provide multiple offerings. In the following chart we align the Cloud Vendor offerings within the two-tier big data system taxonomy defined in the second section.The following table includes some additional Cloud Vendor offerings as well as open source and selected third party tools that provide similar functionality.

THE TIME IS NOW

Most companies that deployed big data systems and data-centric applications in the past 5-10 years did this on-premise (or colocation) or on top of the Cloud Vendor core infrastructure services including storage, networking, and compute. Much has changed in the Cloud Vendor offerings since these early deployments. Cloud Vendors now provide a nearly complete set of serverless big data services. In addition, more and more companies see the value of Cloud Vendor offerings and are trusting their mission-critical data and applications to run on them. So, now is the time to think about migrating big data applications from on-premise or upgrading bespoke systems built on Cloud Vendor infrastructure services. In order to make the best use of, make sure to get a deep understanding of existing systems, develop a clear migration strategy, and establish a data operations center of excellence.

In order to prepare for migration to Cloud Vendor big data offerings, it is necessary for an organization to get a clear picture of its current big data system. This can be difficult depending on the heterogeneity of existing systems, the types of data-centric products it supports, and the number of teams or people using the system. Fortunately, tools (such as Unravel) exist to monitor, optimize, and plan migrations for big data systems and pipelines. During migration planning it is common to discover inefficient, redundant, and even unnecessary pipelines actively running, chewing up compute, and costing the organization time and money. So, during the development of a migration strategy companies commonly find ways to clean up and optimize their data pipelines and overall data architecture.

It is helpful that all three Cloud Vendors are interested in getting a company’s data and applications onto their platforms. To this end, they provide a variety of tools and services to help move data or lift and shift applications and databases onto their platforms. For example, AWS provides a Migration Hub to help plan and execute migrations and a variety of tools like the AWS Database Migration Service. Azure provides free Migration Assessments as well as several tools. And, GCP provides a variety of migration strategies and tools like Anthos and Velostrata depending on a company’s’ current and future system requirements.

Please take a look at the Cloud Vendor migration support sites below.

• Google Cloud Platform (GCP)
• Microsoft Azure
• Amazon AWS

No matter whether a company runs an on-premise system or a fully managed serverless environment, or some hybrid combination, companies need to build expertise a core competence in data operations. DataOps is a rapidly emerging discipline that companies need to own–it is difficult to outsource. Most data implementations utilize tools from multiple vendors, maintain hybrid cloud/on-premises systems, or rely on more than one Cloud Vendor. So, it becomes difficult to rely on a single company or Cloud Vendor to manage all the DataOps task for an organization.

Typical scope includes:

• Data quality
• Metadata management
• Pipeline optimization
• Cost management and Charge back
• Performance Management
• Resource Management
• Business stakeholder Management
• Data governance
• Data catalogs
• Data security & compliance
• ML/AI model management
• Corporate metrics and reporting

Where ever you are on your cloud adoption and workload migration journey, now is the time to start or accelerate your strategic thinking and execution planning for Cloud based data services. Serverless offerings are maturing quickly and give companies faster time to value, increased standardization, and overall lower people and technology costs. However, as migration goes from planning to reality, ensure you invest in the critical skills, technology and process changes to establish a data operations center of excellence.

The post The Guide To Understanding Cloud Data Services in 2022 appeared first on Unravel.

Today, more than 10,000 enterprise businesses worldwide use a complex stack composed of a combination of distributed systems like Spark, Kafka, Hadoop, NoSQL databases, and SQL access technologies.

At Unravel, we have worked with many of these businesses across all major industries. These customers are deploying modern data applications in their data centers, in private cloud deployments, in public cloud deployments, and in hybrid combinations of these.

This paper addresses the requirements that arise in driving reliable performance in these complex environments. We provide an overview of these requirements both at the level of individual applications as well as in holistic clusters and workloads. We also present a platform that can deliver automated solutions to address these requirements as well as taking a deeper dive into a few of these solutions.

The post AI-Powered Data Operations for Modern Data Applications appeared first on Unravel.

Data professionals go through gyrations to extract, ingest, move, clean, format, integrate, transform, calculate, and aggregate data before releasing it to the business community. These “data pipelines” are inefficient and error prone: data hops across multiple systems and is processed by various software programs. Humans intervene to apply manual workarounds to fix recalcitrant transaction data that was never designed to be combined, aggregated, and analyzed by knowledge workers. Business users wait months for data sets or reports. The hidden costs of data operations are immense.

Read this guide to learn how DataOps can streamline the process of building, changing, and managing data pipelines.

The post Eckerson Report Best Practices in DataOps appeared first on Unravel.