It’s the new year and everybody is still living in the wake of the COVID-19 pandemic. We all need a friend in times of trouble and this is no different in the case of business organizations.
This year, 2021, the friend that your company needs more than ever, especially in these trying times, is data.
Given the disruption that this virus caused in the preceding year, enterprises need to start (if they haven’t already) befriending their own internal data, and perhaps external data as well if they are to at least stay viable and at most grow.
The following are some insights from respected data management leaders on how to make friends with your data this year:
Eran Vanounou, CEO at Varada
“Data warehouses are not going to disappear. Data warehouses will continue to be an important legacy technology that organizations will use for mission-critical business applications well into the future.
With the transition to the cloud, data warehouses got a fresh new look and offer some modern attractive capabilities including self-service and serverless.
With the rise of the cloud, data lakes are the new kid on the block. Data lakes are becoming a commodity, a legacy technology in their own right. Their rapid emergence from the innovation stage means two things going forward.
First, organizations will demand simpler, easier to manage, and more cost-effective means of extracting usable business intelligence from their data lakes, using as many data sources as possible.
Second, those same organizations will want the above benefit to be delivered via tools that do not lock them into proprietary data management platforms.
In short, 2021 will begin to see the rapid introduction and evolution of tools that allow users to keep their data lakes in one place and under their control while driving performance up and cost down.”
Paige Roberts, Open-Source Relations Manager at Vertica
“Distributed analytical databases and affordable scalable storage are merging into a single new thing called either a unified analytics warehouse or a data lake house depending on who you’re talking to.
Data lake vendors are scrambling to add ACID capabilities, improve SQL performance, add governance, resource management, security, lineage, and all the things that data warehouse vendors have been perfecting for the last three or four decades.
During the ten years, while data lake software has been coalescing, analytical databases have seen their benefits and added them to their existing stacks: unlimited scale, support for widely varied data types, fast ingestion of streaming data, schema-on-read, and machine learning capabilities.
Just like a lot of things used to claim to be cloudy before they really were, some vendors will claim to be a unified analytics warehouse when they’ve just jammed the two architectures together into a complicated mess, but everyone is racing to make it happen for real.
I think the data warehouse vendors have an unbeatable head start because building a solid, dependable analytical database like Vertica can take ten years or more alone.
The data lake vendors have only been around about ten years, and are scrambling to play catch-up.”
Raj Verma, CEO at SingleStore
“One single SQL query for all data workloads
The way forward is based not only on automation but also on how quickly and widely you can make your analytics accessible and shareable.
Analytics gives you a clear direction of what your next steps should be to keep customers and employees happy, and even save lives. Managing your data is no longer a luxury, but a necessity–and determines how successful you or your company will be.
If you can remove the complexity or cost of managing data, you’ll be very effective.
Ultimately, the winner of the space will take the complexity and cost out of data management, and workloads will be unified so you can write one single SQL query to manage and access all workloads across multiple data residencies.”
Prat Moghe, CEO at Cazena
“Expect more enterprises to declare the battle between data lakes and data warehouses over in 2021 – and focus on driving outcomes and modernizing.
Data warehouses can continue to support reporting and business intelligence, while modern cloud data lakes support all analytics, AI and ML enablement far more flexibly, scalably, and inexpensively than ever – so enterprises can go transform quickly.
Cloud migrations and related cloud data lake implementations will get demonstrably faster and easier as DIY approaches are replaced by turnkey SaaS platforms.
Such solutions will slash production cloud data lake deployment times from months to minutes while controlling costs and providing the continuous operations, security and compliance, AI and ML enablement, and self-service access required for modern analytics initiatives.
That means that migrations that used to take 9-12+ months are complete in a fraction of the time.”
Adi Paz, CEO at GigaSpaces Technologies
“Co-locating analytics and operational data results in faster data processing to accelerate actionable insights and response times for time-sensitive applications such as dynamic pricing, hyper-personalized recommendations, real-time fraud and risk analysis, business process optimization, predictive maintenance, and more.
To successfully deploy analytics and ML in production, a more efficient Data Architecture will be deployed, combining OLTP (CRM, ERP, billing, etc.) with OLAP (data lake, data warehouse, BI, etc.) systems with the ability to build the feature vector more quickly, and with more data for accurate, timely results.”
To summarize the various points made by these industry pundits:
Is there a data management platform that possesses all these qualities and has a proven track record in Fortune 500 companies?
Yes, there is. It’s called Greenplum. Read about it here.