Over the years, enterprises realized that their many isolated systems generate a vast amount of data. What if they could put all that massive data into one centralized platform, correlate it and analyze it? Surely that would uncover a wealth of relevant, hidden business insights. Of course, those were the eighties (actually earlier), so this new platform was called the Data Warehouse. And it was good. So, over the years, it became very popular and by the end of the nineties nearly every large organization had one or dozen of them.
However, with all the goodness, there were some challenges. Data warehousing required a lot of specialized skills and tools. For example, regular databases couldn’t really support very large data warehouses, so from a certain scale, it required specialized and expensive products – like those from Teradata. As a response to the quick adoption of the data warehouse, the “classical” MPP databases arrived to the market about a decade ago, first Netezza and later the rest of the gang (Vertica, Greenplum etc). They were all relatively low cost, and built parallel processing on top of shared-nothing cluster architecture. They were the one place were you could throw “petabytes of data” and analyze it over a large, mostly-commodity, computing cluster.
So, what happened?
Simply, over the time the market gained experience and its requirements and expectations have evolved. For example, some examples of challenges not solved by existing MPP databases include:
- The main challenge of a data warehouse is making sense of the data. The classical DW solution is to clean up and standardize the data as it is being loaded (ETL). This is required as the regular MPP database schema is rigid, and schema evolution is hard and painful (just like all relational databases). That standard method is, however, complex and very time consuming, which makes it very hard for the DW to adapt to the constant and frequent changes in its source systems.
Nowadays, a common requirement is to support dynamic schemas / schema-on-read, so at the very least the frequent schema and data sources changes wont block the ingestion of data.
This also supports the on-going shift of power from DBAs to the developers.
- The classical MPP databases have relatively rigid HA and scalability – unlikely that you could add a couple of nodes every week or every month in real life or survive a “once an hour” node failure rate etc. In other words, MPP databases provided scalability but not elasticity, plus HA that is focused on surviving a single node failure.
Today elasticity is a requirement – inspired by “failure is a common, normal event” mentality of Hadoop and of relying on lower-end commodity servers.
The next step for this mentality will be to also replace the DR concept with a native multi-data-center active-active support as part of the core architecture, which some of the NOSQL and NewSQL players are advocating.
- If talking about rigidity, the existing players usually had an assumption of isolation and uniformity – uniform physical nodes, dedicated high performance cluster interconnect and relatively homogenous workload.
Nowadays with cloud deployments or deployments on a shared Hadoop cluster (maybe virtualized in the future), those assumptions needs to be revisited and products needs to take into account never-ending fluctuation of CPU, I/O and network on shared resources.
- Finally, MPP databases are propriety, closed-source beasts. That doesn’t fit well with the collaborative open-source ecosystem led by the major web giants which are the main driver behind the existing big data boom, innovation and fast pace. This represents a huge mentality shift – just think why MapR doesn’t own the Hadoop market even though it came with a superior offering early on.
Speaking of mentality shift – one more thing to add .Most organizations that will start implementing a solution increasingly ask for a single “big data” platform – that must support complex SQL but also online/OLTP lookups, batches processing unstructured data, free-text searches, graph analysis and whatever else comes up next (future-proofing). For that, the industry is no doubt standardizing on Hadoop as the unifying infrastructure – and will converge around YARN + HDFS infrastructure. Now, no one wants start its big data journey with two big data platforms, so even if sometimes it could have made sense, it will be very hard to promote such solution at the current industry buzz level. The only exception might be SAP Hana, which built a unique offering of merging OLTP and DW into a single MPP system – skipping the ETL headache altogether (while also of course supports integration with Hadoop).
So, what will happen?
Well, looking at my crystal ball, it seems that popularity will continue to quickly shift to modern MPP databases on top of Hadoop.They will have flexible schema (which sucks, but it’s the only way to keep up with the schema changes and data sources proliferation). They will be significantly less monolithic and will leverage the Hadoop ecosystem instead – for example HDFS for storage management, YARN for resource management, HCatalog for metadata management, maybe even support for various open on-disk file formats etc. So, they will be mostly trimmed down to parallel query optimization and runtime engines.
As for the old MPP players, they would sooner or later try to adapt to this world, but for most of them it is simply too late already. The only player who have committed to such change is Greenplum (now: Pivotal) which started migrating its MPP database to HDFS (and renamed it to HAWQ). Even for them, even though their first release is now out, handling the challenges mentioned will not be easy. But it is still early in the game, and their lead (in my opinion) in query optimization over the rest of the “SQL on/in Hadoop” players may buy them enough time to evolve into a truly leading “next-gen” MPP-on-Hadoop player.