A Modern Data Warehouse Architecture: Part 3 – Build an EDW Annex

In the first two post of this series (here and here) I first suggested that Hadoop could be effectively used as the platform for staging and then suggested that a modern warehouse would have a federation layer that turned it into a logical data warehouse. Figure 3 depicts this extended architecture.

Figure 3. A Logical EDW

But if we have both Hadoop and a federation layer implemented… and we recognize the economics associated with moving data to Hadoop… Hadoop provides a 5X-50X price advantage over a commercial very large DBMS product… and we can move data from the expensive environment to the low-cost environment without impacting any applications… then we have the opportunity to move governed EDW data to Hadoop and place it into a Hadoop EDW Annex. Figure 4 shows this.

An EDW Annex

Now you might suggest that there is an impact… Hadoop will be significantly slower than a commercial EDW platform (for now…). But experienced EDW architects understand that in the classic architecture we had to co-locate data in a single database to join the data. So, we put all of the data, hot and cold data, in our EDW even though the service levels required for queries that touch old cold historical data did not justify the power and price of the EDW infrastructure. We had to but did not need to. We knew, if only implicitly, that most EDW queries touch a small subset of the data. Following the ratio suggested by Teradata (see the reference here) that 90% of the queries touch only 20% of the data we can imagine a system where 80% of the data resides in Hadoop to service 10% of the queries… and only that 10% experiences Hadoop performance.

I suggested this approach for Teradata here… but an architecture with an EDW Annex to store cleansed governed historical data works for any expensive RDBMS that can federate with Hadoop: Exadata, Netezza, Teradata, or HANA.

This concludes this series… sort of. I’ll post soon to express more about how this architecture provides long-term strategic value. I think that these three concepts: Hadoop as an EDW staging area, federation and logical data warehousing, and Hadoop as an EDW Annex; provide the foundation for a modern EDW… and I imagine that over the next several years this will become the reference architecture most of us will build to.

A Modern Data Warehouse Architecture: Part 2 – Make it a Logical Data Warehouse

In the first post of this series (here) I suggested that Hadoop could be effectively used as the platform for staging. Figure 2 describes the result. In this post I will extend the architecture by adding a data federation feature and turning the entire picture into a logical data warehouse… and then we’ll consider the consequences.

Figure 2. Add a Data Lake

Figure 3 shows this extension.. but lets quickly review what a federation fabric provides (for more detail on this please see the series that starts here).

Figure 3. Add a Federation Layer

First, the fabric allows any of the tables and files in the picture to be registered as “virtual” tables. This includes tables in EDWs, and any marts, in one or more Hadoop systems, the sandbox, and even in the source systems. From here the fabric is viewed by any programs in the BI, Analytics, Apps layer as a single relational database composed of all of the registered tables.This fabric would consume ODBC and JDBC queries at a minimum and provide some level of data translation, function translation, and query translation to allow all of the virtual tables to be queried through the single SQL dialect offer by the fabric. Finally, the fabric would provide some measure of optimization to reduce the overhead of accessing these distributed systems. It is this optimization that is the main topic of the series I suggested above.

One of the important implications of this… one that is often overlooked before implementation… is that the queries emitted from the fabric add workload to the underlying databases. If, in Figure 3, the top EDW is 100% busy servicing un-federated queries then adding a workload that joins that data to the 2nd EDW’s data will overburden the system. This is why it is not usually sound design to integrate source, OLTP systems into a fabric. The OLTP systems are not likely to be optimized in any way for the resulting workload.

But the upside of the fabric is significant. Consider:

  • If we just said “yes” and loaded ungoverned data into a sandbox the business users can immediately access that data and join it to dimensions and facts deployed elsewhere in the enterprise.
  • Rogue data marts can now be integrated back into the fold.
  • Redundant data deployed to allow joins within a single database instance can be eliminated and the joins can be federated. Note that federation is no silver bullet… there may be performance reasons for co-locating data… but you now can consider the trade-offs (I’ll post later on a way to federate and improve performance).
  • Data bases products can be retired without affecting the programs that access them. If you have an old data mart built on a product that you would rather not support or license… you can move the data and re-point the virtual tables without impacting the tools and applications at all.
  • Data can be relocated based on economics and/or for performance reasons… this will be the subject of the next blog in this series… but as a teaser, remember the economics… Hadoop costs $1K/TB (ok… $1K-$4K) hardware included and commercial databases cost much more.

There is one final advantage to this that is strategic and important enough to deserve to not be buried in the previous bullet list… All of us have seen the database product market move in the last 6+ years from a place where the choices were DB2, Oracle, SQL Server, or Teradata… to a market with those products plus Exadata, plus two flavors of SQL Server, plus Netezza, plus Greenplum, plus Vertica… and then plus Impala, plus Hive, plus Tez, plus Spark, plus MongoDB, plus Cassandra, plus plus plus. I think that it is impossible to place database bets today with the confidence that these bets will pay off five years from now… and certainly there are no bets good for ten years. If you are betting on infrastructure to support the Internet of Things the bets are more risky still. In my opinion a federation layer provides critical insulation from this chaos. With federation you can build applications knowing that you can acquire and retire database products and not affect the queries. IMO this insulation is a strategic imperative…

Sorry… I got a little excited… I don’t usually spew foam. Let me try again. IMO you should seriously consider the benefits of federation in both your EDW architecture and in your enterprise data architecture.

Part 3 of this series considers a new extension to the architecture by adding an EDW Annex… you can see it here.


A Modern Data Warehouse Architecture: Part 1 – Add a Data Lake

In this series I will present a perspective on how the classic EDW architecture is changing under the influence of new technologies, new requirements, and new economics.

Part 1 will show the first of three architectural changes by introducing a data lake into the picture… in Part 2 we’ll extend the picture by adding a logical EDW layer… and in Part 3 we’ll consider the implications of Hadoop as an EDW Annex.

Figure 1. A Classic EDW

Figure 1 describes a classic EDW architecture with sources feeding a staging area, transformations feeding cleaned and certified data to the EDW, and data consumed by analytic applications. In this picture we assume that data marts are part of the EDW fabric and that it is the responsibility of the applications to know which database to query. In most enterprises there are rogue data marts with ungoverned data and these are an issue… but more on that.

Note that Figure 1 also represents an optional sandbox area where uncertified and un-governed data can be landed This helps avoid paying the cost of data governance upfront… before data has proven it has value. A sandbox provides support for rogue data marts and cubes. Obviously use of uncertified data introduces issues… but many believe, as I do, that the trade-off of agility over governance can be productive under the right circumstances.

Figure 2. An EDW with a Data Lake

Figure 2 represents a step towards a modern EDW architecture with a Hadoop-based data lake replacing the staging area as well as providing support for a sandbox.

A data lake provides all of the same capabilities as the staging area it replaced… it may be a landing zone for untransformed data… but it has several other important characteristics:

  • A data lake can hold raw data forever rather than store it temporarily.
  • A data lake has compute included so it can execute transformations and before a single platform for staging and ETL.
  • A data lake has compute and tools included so it can be used to analyze raw data for trends and anomalies.
  • A data lake can easily store semi-structured and unstructured data.
  • A data lake can store big data.

Of course a staging area based on a RDBMS could do the same… but the economics are completely different. A Hadoop system provides storage and processing for as little as $1K/TB; the cost of an EDW hardware and software ranges from $15K/TB to $50K/TB… 15X to 50X more.

In addition, the data lake provides a cost-effective, extensible platform for building more sandboxes. If, by using Hadoop, you lower the upfront cost of a sandbox to $1K/TB then, when combined with the cost reduction from postponing the development of data quality and governance processes; you may find yourself in a position where you can just say “yes” to requests to add new data. At $1K/TB is may be unnecessary to force the business to build a business case and perform the difficult intellectual challenge of developing an ROI argument for data that has never been available and therefore has an unproven value proposition. Later, once the value is proven, you might move it to a governed state and to a governed platform.

Hadoop as an EDW staging area is not a new concept (see here)… the ETL vendors are supporting Hadoop as an ETL engine already. But a Hadoop staging approach starts to solve one of the nagging problems with the classic architecture: the lack of agility. Just saying “yes” to new data and corralling rogue marts provides a foundation to experiment and evolve while also providing the means to leverage successful experiments across the Enterprise.

There is more required to satisfy sandbox users… on to that in Part 2 – Logical Data Warehousing here.


  • Wikitionary definition of a Data Lake here.
  • Informatica Big Data Edition here.
  • Curt Monash on IBM ETL here.
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