This is the first in a multi-part series of blogs discussing Hadoop distribution differences to help enterprises focus on the important factors involved in choosing a Hadoop distribution—without hype or marketing spin. Zaloni has a long history of helping companies gain tangible business value from Hadoop, regardless of the distribution.
For enterprises looking for ways to more quickly ingest data into their Hadoop data lakes, Kafka is a great option. What is Kafka? Kafka is a distributed, scalable and reliable messaging system that integrates applications/data streams using a publish-subscribe model. It is a key component in the Hadoop technology stack to support real-time data analytics or monetization of Internet of Things (IOT) data.
You’ve heard it time and time again: cloud is the future; those who don’t adopt modern big data practices will fall behind the pack; the next wave of IT disruption is right around the corner. And yet, at the same time, budgets are shrinking, demand is growing and pressure on the IT organization to show value is at an all-time high. As an executive, you have the full force of your business behind you and more options than ever to achieve both short- and long-term goals with business data. So many options, in fact, that the landscape has become a confusing, often contradictory mess of competing solutions.
IT analyst, research and strategy firm Enterprise Strategy Group (ESG) recently did an economic value validation report of our Bedrock data lake management platform (disclosure: we commissioned it) and we wanted to share the results. I recommend you read the report itself, but here are the highlights.
As the volume and variety of data continues to grow exponentially, it's imperative that organizations are able manage and govern their data in a way that's scalable and cost-effective. Many early adopters used data lakes as a relatively inexpensive storage solution and dumped data into them without much of a plan. Now these enterprises realize that in order to derive true business value from the data lake, they need to employ proactive data management best practices.
Here’s a look into the future of the enterprise data ecosystem: the modern data architecture will have a managed data lake at its core. It will be fed by various structured data sources, real-time data streams, such as from the Internet of Things, and unstructured data like emails, videos, photos, audio files, presentations and more.
Are you considering modernizing your data architecture to derive more value from big data? Then it’s likely that you are considering a data lake architecture. Wherever you are in your journey, we at Zaloni have developed a list of high level considerations in our ‘Big Data Lake Checklist for Success’. Use this list as a communication tool on your data lake journey to inform and engage diverse stakeholders across your organization, from the C-Suite (CDO, CIO, CTO) to developers and lines of business.
A data lake is a central location in which to store all your data, regardless of its source or format. It is typically, although not always, built using Hadoop. The data can be structured or unstructured. You can then use a variety of storage and processing tools—typically tools in the extended Hadoop ecosystem—to extract value quickly and inform key organizational decisions.
Topics: Big Data
If it’s not broken, don’t fix it. These are the words most of us tend to live by. However, for manufacturers, this approach can result in significant cost – not only for equipment maintenance, but from loss in production from unplanned equipment downtime. Although it seems obvious that it pays to take a more proactive approach to maintenance, many manufacturers still use basic consoles for monitoring assets and strive to fix a problem as quickly as possible after it’s detected. Among other drawbacks, basic monitoring consoles provide isolated views of equipment condition, and managing them is time-consuming, resource intensive and often results in generation of false alerts.