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Final week Teradata provided its long-awaited response to the emergence of the data lakehouse. As VentureBeat’s George Lawton reported, Teradata has all the time differentiated itself by stretching the capabilities of analytics, first with massively parallel processing by itself specialised machines, and extra not too long ago, with software-defined home equipment tuned for variations in workloads — from compute-intensive to IOPS (enter/output operations per second)-intensive. And because the acquisition of Aster Information Programs over a decade in the past, Teradata morphed from fixing massive analytics issues to fixing any analytics drawback with a various portfolio of analytic libraries stretching SQL to new areas comparable to path or graph analytics.

With the cloud, we’ve been ready for when Teradata would absolutely exploit cloud object storage, which is the de facto data lake. So the twin bulletins final week of VantageCloud Lake Version and ClearScape Analytics have been logical subsequent steps on Teradata’s journey to the info lakehouse. Teradata is lastly making cloud storage a first-class citizen and opening it as much as its huge analytics portfolio.

However not like Teradata’s earlier strikes to parallelized and polyglot analytics, the place it led the sphere, this time with the lakehouse, it has firm. The announcement won’t have talked about the lakehouse phrase, however that’s what it was all about. As we noted several months back, nearly everybody within the knowledge world together with Oracle, Teradata, Cloudera, Talend, Google, HPE, Fivetran, AWS, Dremio and even Snowflake has felt compelled to answer Databricks, which launched the info lakehouse.

Teradata’s path to the info lakehouse

Nonetheless, Teradata approaches the info lakehouse with some distinctive twists and is all about optimization. Teradata’s secret sauce has all the time been about extremely optimized compute, interconnects, storage and question engines, together with workload administration designed to run compute sources as much as 95% utilization. When commodity {hardware} acquired adequate, Teradata launched IntelliFlex the place efficiency and optimizations could possibly be configured by means of software program. The potential to optimize for {hardware} not-invented-here opened the door to Teradata optimizing for AWS, and down the street, the opposite hyperscalers.


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Teradata launched VantageCloud a 12 months in the past, and late final 12 months ran a 1,000+ node benchmark that no different cloud analytics supplier has thus far matched. However this was for a extra standard data warehouse utilizing customary block storage.

The complication in making the lakehouse occur was creating a desk format for knowledge sitting in cloud object storage. That enables all of the niceties related to knowledge warehouses, comparable to ACID transactions, that are key to making sure consistency of knowledge, extra granular safety and entry controls, and uncooked efficiency. Databricks fired the primary shot with Delta Lake, and extra not too long ago, different suppliers from Snowflake to Cloudera and others have embraced Apache Iceberg, the frequent thread being that that is all based mostly on open supply expertise. For Lake Version, Teradata went its personal method with its personal knowledge lake desk format, which the corporate claims delivers superior efficiency in comparison with Delta and Iceberg.

The opposite aspect of the lakehouse coin is software program. Apart from its SQL engine, which has been designed to deal with giant, advanced queries that may be part of as much as a whole lot of tables, Teradata has a big portfolio of analytic libraries that run in-database. This has been one among Teradata’s best-kept secrets and techniques. Largely the legacy of the Aster Information acquisition over a decade in the past, these analytics have been specifically tuned to take advantage of the underlying parallelism, and so they went effectively past SQL, encompassing capabilities comparable to n-Path, graph, time sequence evaluation, and machine studying, all accessed by means of SQL extensions.

Formally branding the portfolio as ClearScape Analytics, Teradata is lastly drawing consideration to the truth that it’s a holistic analytics platform and never merely an information warehouse, knowledge lake or lakehouse. As a part of the announcement, Teradata beefed up the time sequence and MLOps content material. However after we take care of the info lake, knowledge scientists are very opinionated on selecting their very own languages or instruments. And so, VantageCloud may even help a ring-our-own-analytics choice for these preferring to write down Python and work from Jupyter notebooks or their very own workbenches, and presently has integrations with Dataiku, KNIME and Alteryx. ClearScape analytics might be obtainable, each for VantageCloud Lake Version and the usual Enterprise Version.

Lake Version and ClearScape Analytics are promising begins for Teradata as knowledge lakehouse. There’s little query that Teradata’s scale and help of polyglot analytics made lakehouse a query of when, not if. And branding the analytics portfolio is greater than only a advertising train, because it lastly shines the highlight on what had been a well-kept secret: Teradata’s differentiation goes past the optimized SQL engine and infrastructure to incorporate analytics optimized for that engine. VantageCloud takes the analytics portfolio full circle by unleashing the portfolio on cloud object storage, and, with usage-based pricing, doubtlessly opens up the portfolio for extra discretionary workloads in comparison with the times when prospects have been operating on-premises with agency ceilings on capability.

A want checklist for Teradata

That leaves our want checklist for what Teradata ought to do subsequent. In abstract, we need to see Teradata enterprise additional out of its consolation zone to attract new audiences of customers. Admittedly, with the lakehouse, the problem just isn’t distinctive to Teradata, as Databricks, for instance, seems to attract in enterprise analysts whereas Snowflake courts knowledge scientists.

To attract that new viewers, Teradata ought to decrease entry boundaries and put open supply on a extra degree footing with its proprietary atmosphere. With Lake Version, Teradata has dramatically lowered its entry pricing to $5,000/month. That could be a marked drop from the six- and seven-figure annual contracts that Teradata prospects sometimes pay, however we’d wish to see Teradata go additional with a freemium providing that permits new customers to kick the tires. Heck, even incumbents not recognized for low cost pricing like Oracle have embraced free tiers.

As for open supply, there are a few pathways that we’d wish to see Teradata additional develop. The primary is drawing non-Teradata customers to ClearScape Analytics by means of optimized APIs to open supply Delta and/or Iceberg knowledge lakes. Whereas efficiency won’t be on par with Teradata’s personal knowledge lake desk format, it could possibly be made “adequate.”

Conversely, we’d wish to see parallel efforts with so-called BYO analytics, drawing the Python crowd by means of optimized APIs with Teradata’s personal knowledge lake desk format. As an illustration, we wish to see Teradata staff up with Anaconda for juice efficiency of the Conda Python library portfolio, a lot as Anaconda is already doing with Snowflake. On the finish of the day, it’s all in regards to the analytics.

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