The rapid proliferation and increased volume of data across industries has magnified the need for organizations to have a solid strategy in place for processing and managing real-time data. Improving overall data capabilities enables teams to operate more efficiently, and emerging technologies have even created a smoother pathway for bringing real-time data closer to business users, which plays a critical role in effective decision-making.
This Refcard focuses on architecture patterns for real-time data capabilities, beginning with an overview of data challenges, performance, and security and compliance. Readers will then dive into the key data architecture patterns — their components, advantages, and challenges — setting the stage for an example architecture, which demonstrates how to use open-source tools for a common real-time, high-volume data use case.
Twenty years ago, your data warehouse probably wouldn’t have been voted hottest technology on the block. These bastions of the office basement were long associated with siloed data workflows, on-premises computing clusters, and a limited set of business-related tasks (i.e., processing payroll, and storing internal documents).
Now, with the rise of data-driven analytics, cross-functional data teams, and most importantly, the cloud, the phrase “cloud data warehouse” is nearly analogous to agility and innovation.
Data Fabric provides a single environment to access data and enable simpler and unified data management by connecting data from disparate sources. Such a data architecture helps businesses to discover unique, business-relevant relationships between the available data points.
Data fabrication is an architecture that is developed to standardize data management practices across multiple environments cloud, on-premises, and edge devices. Data fabric is listed among Gartner’s top 10 Data and Analytics trends. It also allows enterprises to cost-effectively leverage technologies such as artificial intelligence and IoT by reducing time for integration design by 30%, deployment by 30%, and maintenance by 70%. With the increasing scalability and complexity of data from disparate environments in enterprises, a data fabric architecture helps to take the data to the right place and at the right time to make business analysis easy. Thus, it helps to unleash the power of data to meet business demands.
This article highlights the synergy between the two widely adopted open-source projects, Alluxio and Presto, and demonstrates how together they deliver a self-serve data architecture across clouds.
What Makes an Architecture Self-Serve?
Condition 1: Evolution of the Data Platform Does Not Require Changes
All data platforms evolve over time, including the addition of a new data store, compute engine, or a new team that needs to access shared data. In either case, a data platform is self-serve if it does not require changes to accommodate evolution.
From intrusion detection to threat analysis to endpoint security, the effectiveness of cybersecurity efforts often boils down to how much data can be processed in real-time with the most advanced algorithms and models.
Many factors are obviously involved in stopping cybersecurity threats effectively. However, the databases responsible for processing the billions or trillions of events per day (from millions of endpoints) play a particularly crucial role. High throughput and low latency directly correlate with better insights as well as more threats discovered and mitigated in near real-time. Cybersecurity data-intensive systems are incredibly complex: many span 4+ data centers with database clusters exceeding 1000 nodes and petabytes of heterogeneous data under active management.
Modern businesses depend on data to make strategic decisions, but today’s enterprises are seeing an exponential increase in the amount of data available to them. Churning through all this data to get meaningful business insights means there’s very little room for data discrepancies. How do we put in place a robust data quality assurance process?
Fortunately for today’s DataOps engineers, tools like Datafold and dbt (Data Build Tool) are simplifying the challenge of ensuring data quality. In this post, we’ll look at how these two tools work in tandem to bring observability and repeatability into your data quality process.
I've been working as Solution Architect for many years, and I've seen the same mistakes very often. Usually, companies want to evolve their data platform because the current solution doesn't cover their needs, which is a good reason. But many times they start from the wrong starting point:
Identify the requirements and the to-be solution but forget the retrospective step of the current solution.
Identify certain products as the main problems. Often this involves choosing another product as the magic solution to resolve these problems.
Identify technological silos but no knowledge silos, therefore do not support enough the data governance solutions.
To not plan thoroughly a coexistence plan between the current solution and the new one. The migrations never end, and the legacy products never switch off.
I think the reasons are simple, and at the same time are very difficult to change:
This article launches a new series exploring a retail architecture blueprint. It's focusing on presenting access to ways of mapping successful implementations for specific use cases.
It's an interesting challenge creating architectural content based on common customer adoption patterns. That's very different from most of the traditional marketing activities usually associated with generating content for the sole purpose of positioning products for solutions. When you're basing the content on actual execution in solution delivery, you're cutting out the chuff.
It's been a few months since I was thinking about writing "What's a New Enterprise Data Platform?" In the last few years, I've been working as a Data Solution Architect and Product Owner for a new data platform; I've learned a lot and I would like to share my experiences with the community.
I'm not going to write about the Data-Driven approach, but how to build a platform that allows a company to implement it. When we design and build a Data Platform, we are working on providing the capacities and tools that others teams need to develop their projects. I am not forgetting the data but I think the data should be a service, not a product.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
"Data and analytics for all!” — the admirable, new mantra for today’s companies. But it’s not easy to put all of an organization’s analytical data and assets into the hands of everyone that needs it. That’s why embarking on this democratization initiative requires you to be prepared to overcome the five monumental challenges you undoubtedly will face.
Join us for this interactive webcast where we will: explore the recommended components of an all-encompassing, extended analytics architecture; dive into the details of what stands between you and data democratization success, and; reveal how a new open data architecture maximizes data access with minimal data movement and no data copies.
If selling products online is a core part of your business, then you need to build an e-commerce data model that’s scalable, flexible, and fast. Most off-the-shelf providers like Shopify and BigCommerce are built for small stores selling a few million dollars in orders per month, so many e-commerce retailers working at scale start to investigate creating a bespoke solution.
To make good decisions you need good data. Currently 2.5 quintillion bytes of data are created each day, and the amount of data will continue to increase exponentially as the digital economy matures. But it’s not enough to have data — it needs to be data that you can get and process in real-time that is most valuable for today’s digital apps and services. IDC's “Data Age 2025” whitepaper, predicts that nearly 30% of the global datasphere will be real-time by 2025.
The challenge, irrespective of how big or small your company might be, is scale. Scaling for extreme growth — moving from tens-to-hundreds of terabytes to petabytes is both an art and science.
Current market dynamics don’t allow for slowdowns. Digital disrupters have made use of innovations in AI, serverless data platforms, and seamless analytics that have completely upended traditional business models. The current market challenges presented by the Covid-19 pandemic have only exacerbated the need for fast, flexible service offerings. To remain competitive and relevant, businesses today have to move quickly to deploy new data technologies alongside legacy infrastructure to drive market-driven innovations such as personalized offers, real-time alerts, and predictive maintenance.
However, as businesses strive to implement the latest in data technology—from stream processing to analytics and data lakes—many find that their data architecture is becoming bogged down with large amounts of data that their legacy programs can’t efficiently govern or properly utilize.
There has been much said about the importance of security, availability, integration, analysis, and access when it comes to the role of data in today's enterprise world. But you talk about data performance and the need to embrace performance as a philosophy.
Is this in the context of a piece of software code or of wider digital transformation in an organisation?
The fourth Industrial Revolution brought cyber-physical systems to the manufacturing floor, leading to to the production of data at an unforeseen volume. Most of the current statistical process control systems (SPCs) were designed in the Industry 3.0 era, and they use only a fraction of the data produced on production lines to monitor the production quality.
However, to ensure waste reduction and yield increases, new age manufacturing systems need real-time operational data replication and analysis to improve. This article lists the key design considerations for real-time operational data architecture.
This blog is about how I used Alluxio to reduce p99 and p50 query latencies and optimized the overall platform costs for a distributed querying application. I walk through the product and architecture decisions that lead to our final architecture, discuss the tradeoffs, share some statistics on the improvements, and discuss future improvements to the system.
Imagine this familiar situation: as an analyst in your company, you've been tasked with the daunting task of assimilating all of your organization's data to collect unique and comprehensive insights. But this is easier said than done. Business development has much of their data siloed into a proprietary CRM solution, finance keeps theirs hidden away in spreadsheets, and application developers have SDK and IoT data streaming in to separate on-prem databases with no fault-tolerance built in. On top of that, compliance and security issues were never even considered. There seems to be no rhyme or reason to how everything works, it's impossible to get a unified view from all of the enterprise data. And "data science" is mostly done around the organization by way of sampling data from different pools and then making a "seat of the pants" guesstimate from arbitrarily sampled data, which is neither productive nor reliable. What a mess!
You need to have a strategy for your data. How will you do this? What data will you collect? Which data will you store — and where? Who is the audience for your data? Who consumes your analyzed data? What kind of access controls and permissions do you want to have on your data?
This article launches a new series exploring a retail architecture blueprint. It's focusing on presenting access to ways of mapping successful implementations for specific use cases.