March 12, 2017

Introduction to SequoiaDB and SequoiaCM

For starters, let me say:

Also:

Unfortunately, SequoiaDB has not captured a lot of detailed information about unpaid open source production usage.

While I usually think that the advantages of open source are overstated, in SequoiaDB’s case open source will have* an additional benefit when SequoiaDB does go international — it addresses any concerns somebody might have about using Chinese technology.

*Edit: Actually, this claim is overstated based on SequoiaDB’s current open source practices. Please see the comment thread below.

SequoiaDB’s technology story starts:

SequoiaDB’s relationship with PostgreSQL is complicated, but as best I understand SequoiaDB’s relational operations:

PostgreSQL stored procedures are already in the SequoiaDB product. Triggers and referential integrity are not. Neither, so far as I can tell, are PostgreSQL’s datatype extensibility capabilities.

I neglected to ask how much of that remains true when SparkSQL is invoked.

SequoiaDB’s use cases to date seem to fall mainly into three groups:

Internet back-ends, however — and this is somewhat counter-intuitive for an open-source JSON store — are rare, at least among paying subscription customers. But SequoiaDB did tell me of one classic IoT (Internet of Things) application, with lots of devices “phoning home” and the results immediately feeding a JSON-based dashboard.

To understand SequoiaDB’s “operational data lake” story, it helps to understand the typical state of data warehousing at SequoiaDB’s customers and prospects, which isn’t great:

SequoiaDB operational data lakes offer multiple improvements over that scenario:

Views are particularly useful with what might be called slowly changing schemas. (I didn’t check whether what SequoiaDB is talking about matches precisely with the more common term “slowly changing dimensions”.) Each time the schema changes, a new table is created in SequoiaDB to receive copies of the data. If one wants to query against the parts of the database structure that didn’t change — well, a view can be establish to allow for that.

Finally, it seems that SequoiaCM uses are concentrated in what might be called “security and checking-up” areas, such:

SequoiaCM deals seem to be bigger than other SequoiaDB ones, surely in part because the amounts of data managed are larger.

Comments

4 Responses to “Introduction to SequoiaDB and SequoiaCM”

  1. Tao Wang on March 12th, 2017 4:06 pm

    > I neglected to ask how much of that remains true when SparkSQL is invoked.

    SparkSQL is one of the most commonly used SQL interfaces for SequoiaDB in batch processing jobs. spark-sequoiadb-connector is implemented by overriding Spark RDD, so that all Spark RDD/Streaming/SQL are able to use SequoiaDB as the data source and target storage engine.

    https://github.com/SequoiaDB/spark-sequoiadb

  2. Curt Monash on March 13th, 2017 8:04 am

    I didn’t check out the specifics of SequoiaDB’s open source story before posting. Let me now fill that gap. The mechanics are basically:

    • SequoiaDB’s development at this time is entirely internal.
    • Every once in a while, SequoiaDB posts a complete version to Github. This is the Community Edition, which will likely be one release behind the Enterprise Edition, but otherwise is the same code.
    • The last SequoiaDB posting to Github was in April, 2016.

    So it was a stretch when I suggested or assumed that there was a robust open source community familiar with the latest release of SequoiaDB’s products.

  3. Tao Wang on March 13th, 2017 10:33 am

    You’re right. In the Chinese market we are behaving very much like a traditional enterprise software vendor, albeit with a subscription-only pricing model. We intend to engage more fully with the open source community as we expand into other geographical regions. We’re sorry for any confusion this might cause!

  4. Tao Wang on March 16th, 2017 9:53 am

    Some people may ask why SequoiaDB is based in China, rather than from North America like most other database companies.

    China is the world’s second largest economy, and its share of the world’s top 500 enterprises is almost equal to North America’s. Each year, massive amounts of data are generated in financial, telecom, internet, government and many other industries.

    While many new distributed database companies are still trying to focus on customers in North America at the moment, SequoiaDB was founded in 2011 in China, and has helped a large number of Chinese and Asian customers with its leading technologies.

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