Tag: RDBMS

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A New Wave in Application Development

Application development is new again.

The ever-changing business landscape – fueled by digital transformation initiatives indiscriminate of industry – demands businesses deliver innovative customer – and partner – facing solutions, not just tactical apps to support internal functions.

Therefore, application developers are playing an increasingly important role in achieving business goals. The financial services sector is a notable example, with companies like JPMorgan Chase spending millions on emerging fintech like online and mobile tools for opening accounts and completing transactions, real-time stock portfolio values, and electronic trading and cash management services.

But businesses are finding that creating market-differentiating applications to improve the customer experience, and subsequently customer satisfaction, requires some significant adjustments. For example, using non-relational database technologies, building another level of development expertise, and driving optimal data performance will be on their agendas.

Of course, all of this must be done with a focus on data governance – backed by data modeling – as the guiding principle for accurate, real-time analytics and business intelligence (BI).

Evolving Application Development Requirements

The development organization must identify which systems, processes and even jobs must evolve to meet demand. The factors it will consider include agile development, skills transformation and faster querying.

Rapid delivery is the rule, with products released in usable increments in sprints as part of ongoing, iterative development. Developers can move from conceptual models for defining high-level requirements to creating low-level physical data models to be incorporated directly into the application logic. This route facilitates dynamic change support to drive speedy baselining, fast-track sprint development cycles and quick application scaling. Logical modeling then follows.

Application Development

Agile application development usually goes hand in hand with using NoSQL databases, so developers can take advantage of more pliable data models. This technology has more dynamic and flexible schema design than relational databases and supports whatever data types and query options an application requires, processing efficiency, and scalability and performance suiting Big Data and new-age apps’ real-time requirements. However, NoSQL skills aren’t widespread so specific tools for modeling unstructured data in NoSQL databases can help staff used to RDBMS ramp up.

Finally, the shift to agile development and NoSQL technology as part of more complex data architectures is driving another shift. Storage-optimized models are moving to the backlines because a new format is available to support real-time app development. It is one that understands what’s being asked of the data and enables schemes to be structured to support application data access requirements for speedy responses to complex queries.

The NoSQL Paradigm

erwin DM NoSQL takes into account all the requirements for the new application development era. In addition to its modeling tools, the solution includes patent-pending Query-Optimized ModelingTM that replaces storage-optimized modeling, giving users guidance to build schemas for optimal performance for NoSQL applications.

erwin DM NoSQL also embraces an “any-squared” approach to data management, so “any data” from “anywhere” can be visualized for greater understanding. And the solution now supports the Couchbase Data Platform in addition to MongoDB. Used in conjunction with erwin DG, businesses also can be assured that agility, speed and flexibility will not take precedence over the equally important need to stringently manage data.

With all this in place, enterprises will be positioned to deliver unique, real-time and responsive apps to enhance the customer experience and support new digital-transformation opportunities. At the same time, they’ll be able to preserve and extend the work they’ve already done in terms of maintaining well-governed data assets.

For more information about how to realize value from app development in the age of digital transformation with the help of data modeling and data governance, you can download our new e-book: Application Development Is New Again.

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Data Modeling in a Jargon-filled World – In-memory Databases

With the volume and velocity of data increasing, in-memory databases provide a way to keep processing speeds low.

Traditionally, databases have stored their data on mechanical storage media such as hard disks. While this has contributed to durability, it’s also constrained attainable query speeds. Database and software designers have long realized this limitation and sought ways to harness the faster speeds of in-memory processing.

The traditional approach to database design – and analytics solutions to access them – includes in-memory caching, which retains a subset of recently accessed data in memory for fast access. While caching often worked well for online transaction processing (OLTP), it was not optimal for analytics and business intelligence. In these cases, the most frequently accessed information – rather than the most recently accessed information – is typically of most interest.

That said, loading an entire data warehouse or even a large data mart into memory has been challenging until recent years.

In-Memory

There are a few key factors in making in-memory databases and analytics offerings relevant for more and more use cases. One such factor has been the shift to 64-bit operating systems. Another is that it makes available much more addressable memory. And as one might assume, the availability of increasingly large and affordable memory solutions has also played a part.

Database and software developers have begun to take advantage of in-memory databases in a myriad of ways. These include the many key-value stores such as Amazon DynamoDB, which provide very low latency for IoT and a host of other use cases.

Another way businesses are taking advantage of in-memory is through distributed in-memory NoSQL databases such as Aerospike, to in-memory NewSQL databases such as VoltDB. However, for the remainder of this post, we’ll touch in more detail on several solutions with which you might be more familiar.

Some database vendors have chosen to build hybrid solutions that incorporate in-memory technologies. They aim to bridge in-memory with solutions based on tried-and-true, disk-based RDBMS technologies. Such vendors include Microsoft with its incorporation of xVelocity into SQL Server, Analysis Services and PowerPivot, and Teradata with its Intelligent Memory.

Other vendors, like IBM with its dashDB database, have chosen to deploy in-memory technology in the cloud, while capitalizing on previously developed or acquired technologies (in-database analytics from Netezza in the case of dashDB).

However, probably the most high-profile application of in-memory technology has been SAP’s significant bet on its HANA in-memory database, which first shipped in late 2010. SAP has since made it available in the cloud through its SAP HANA Cloud Platform, and on Microsoft Azure and it has released a comprehensive application suite called S/4HANA.

Like most of the analytics-focused in-memory databases and analytics tools, HANA stores data in a column-oriented, in-memory database. The primary rationale for taking a column-oriented approach to storing data in memory is that in analytic use cases, where data is queried but not updated, it allows for often very impressive compression of data values in each column. This means much less memory is used, resulting in even higher throughput and less need for expensive memory.

So what approach should a data architect adopt? Are Microsoft, Teradata and other “traditional” RDBMS vendors correct with their hybrid approach?

As memory gets cheaper by the day, and the value of rapid insights increases by the minute, should we host the whole data warehouse or data mart in-memory as with vendors SAP and IBM?

It depends on the specific use case, data volumes, business requirements, budget, etc. One thing that is not in dispute is that all the major vendors recognize that in-memory technology adds value to their solutions. And that extends beyond the database vendors to analytics tool stalwarts like Tableau and newer arrivals like Yellowfin.

It is incumbent upon enterprise architects to learn about the relative merits of the different approaches championed by the various vendors and to select the best fit for their specific situation. This is something that’s admittedly, not easy given the pace of adoption of in-memory databases and the variety of approaches being taken.

But there’s a silver lining to the creative disruption caused by the increasing adoption of in-memory technologies. Because of the sheer speed the various solutions offered, many organizations are finding that the need to pre-aggregate data to achieve certain performance targets for specific analytics workloads is disappearing. The same goes for the need to de-normalize database designs to achieve specific analytics performance targets.

Instead, organizations are finding that it’s more important to create comprehensive atomic data models that are flexible and independent of any assumed analytics workload.

Perhaps surprisingly to some, third normal form (3NF) is once again not an unreasonable standard of data modeling for modelers who plan to deploy to a pure in-memory or in-memory-augmented platform.

Organizations can forgo the time-consuming effort to model and transform data to support specific analytics workloads, which are likely to change over time anyway. They also can stop worrying about de-normalizing and tuning an RDBMS for those same fickle and variable analytics workloads, focusing on creating a logical data model of the business that reflects the business information requirements and relationships in a flexible and detailed format, that doesn’t assume specific aggregations and transformations.

The blinding speed of in-memory technologies provides the aggregations, joins and other transformations on the fly, without the onerous performance penalties we have historically experienced with very large data volumes on disk-only-based solutions. As a long-time data modeler, I like the sound of that. And so far in my experience with many of the solutions mentioned in this post, the business people like the blinding speed and flexibility of these new in-memory technologies!

Please join us next time for the final installment of our series, Data Modeling in a Jargon-filled World – The Logical Data Warehouse. We’ll discuss an approach to data warehousing that uses some of the technologies and approaches we’ve discussed in the previous six installments while embracing “any data, anywhere.”

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Data Modeling in a Jargon-filled World – Managed Data Lakes

Managed Data Lakes

More and more businesses are adopting managed data lakes.

Earlier in this blog series, we established that leading organizations are adopting a variety of approaches to manage data, including data that may be sourced from a wide range of NoSQL, NewSQL, RDBMS and unstructured sources.

In this post, we’ll discuss managed data lakes and their applications as a hybrid of less structured data and more traditionally structured relational data. We’ll also talk about whether there’s still a need for data modeling and metadata management.

The term Data Lake was first coined by James Dixon of Pentaho in a blog entry in which he said:

“If you think of a data mart as a store of bottled water – cleansed and packaged and structured for easy consumption – the data lake is a large body of water in a more natural state. The contents of the lake stream in from a source to fill the lake, and various users of the lake can come to examine, dive in, or take samples.”

Use of the term quickly took on a life of its own with often divergent meanings. So much so that four years later Mr. Dixon felt compelled to refute some criticisms by the analyst community by pointing out that they were objecting to things he actually never said about data lakes.

However, in my experience and despite Mr. Dixon’s objections, the notion that a data lake can contain data from more than one source is now widely accepted..

Similarly, while most early data lake implementations used Hadoop with many vendors pitching the idea that a data lake had to be implemented as a Hadoop data store, the notion that data lakes can be implemented on non-Hadoop platforms, such as Azure Blob storage or Amazon S3, has become increasingly widespread.

So a data lake – as the term is widely used in 2017 – is a detailed (non-aggregated) data store that can contain structured and/or non-structured data from more than one source implemented on some kind of inexpensive, massively scalable storage platform.

But what are “managed data lakes?”

To answer that question, let’s first touch on why many early data lake projects failed or significantly missed expectations. Criticisms were quick to arise, many of which were critiques of data lakes when they strayed from the original vision, as established earlier.

Vendors seized on data lakes as a marketing tool, and as often happens in our industry, they promised it could do almost anything. As long as you poured your data into the lake, people in the organization would somehow magically find exactly the data they needed just when they needed it. As is usually the case, it turned out that for most organizations, their reality was quite different. And for three important reasons:

  1. Most large organizations’ analysts didn’t have the skillsets to wade through the rapidly accumulating pool of information in Hadoop or whichever new platforms had been chosen to implement their data lakes to locate the data they needed.
  2. Not enough attention was paid to the need of providing metadata to help people find the data they needed.
  3. Most interesting analytics are a result of integrating disparate data points to draw conclusions, and integration had not been an area of focus in most data lake implementations.

In the face of growing disenchantment with data lake implementations, some organizations and vendors pivoted to address these drawbacks. They did so by embracing what is most commonly called a managed data lake, though some prefer the label “curated data lake” or “modern data warehouse.”

The idea is to address the three criticisms mentioned above by developing an architectural approach that allows for the use of SQL, making data more accessible and providing more metadata about the data available in the data lake. It also takes on some of the challenging work of integration and transformation that earlier data lake implementations had hoped to kick down the road or avoid entirely.

The result in most implementations of a managed data lake is a hybrid that tries to blend the strengths of the original data lake concept with the strengths of traditional large-scale data warehousing (as opposed to the narrow data mart approach Mr. Dixon used as a foil when originally describing data lakes).

Incoming data, either structured or unstructured, can be easily and quickly loaded from many different sources (e.g., applications, IoT, third parties, etc.). The data can be accumulated with minimal processing at reasonable cost using a bulk storage platform such as Hadoop, Azure Blob storage or Amazon S3.

Then the data, which is widely used within the organization, can be integrated and made available through a SQL or SQL-like interface, such as those from Hive to Postgres to a tried-and-true commercial relational database such as SQL Server (or its cloud-based cousin Azure SQL Data Warehouse).

In this scenario, a handful of self-sufficient data scientists may wade (or swim or dive) in the surrounding data lake. However, most analysts in most organizations still spend most of their time using familiar SQL-capable tools to analyze data stored in the core of the managed data lake – an island in the lake if we really want to torture the analogy – which is typically implemented either using an RDBMS or a relational layer like Hive on top of the bulk-storage layer.

It’s important to note that these are not two discrete silos. Most major vendors have added capabilities to their database and BI offerings to enable analysis of both RDBMS-based and bulk-storage layer data through a familiar SQL interface.

This enables a much larger percentage of an organization’s analysts to access data both in the core and the less structured surrounding lake, using tools with which they’re already familiar.

As this hybrid managed data lake approach incorporates a relational core, robust data modeling capabilities are as important as ever. The same goes for data governance and a thorough focus on metadata to provide clear naming and definitions to assist in finding and linking with the most appropriate data.

This is true whether inside the structured relational core of the managed data lake or in the surrounding, more fluid data lake.

As you probably guessed from some of the links in this post, more and more managed data lakes are being implemented in the cloud. Please join us next time for the fifth installment in our series: Data Modeling in a Jargon-filled World – The Cloud.

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Data Modeling in a Jargon-filled World – NoSQL/NewSQL

erwin DM NoSQL Model

In the first two posts of this series, we focused on the “volume” and “velocity” of Big Data, respectively.  In this post, we’ll cover “variety,” the third of Big Data’s “three Vs.” In particular, I plan to discuss NoSQL and NewSQL databases and their implications for data modeling.

As the volume and velocity of data available to organizations continues to rapidly increase, developers have chafed under the performance shackles of traditional relational databases and SQL.

An astonishing array of database solutions have arisen during the past decade to provide developers with higher performance solutions for various aspects of managing their application data. These have been collectively labeled as NoSQL databases.

Originally NoSQL meant that “no SQL” was required to interface with the database. In many cases, developers viewed this as a positive characteristic.

However, SQL is very useful for some tasks, with many organizations having rich SQL skillsets. Consequently, as more organizations demanded SQL as an option to complement some of the new NoSQL databases, the term NoSQL evolved to mean “not only SQL.” This way, SQL capabilities can be leveraged alongside other non-traditional characteristics.

Among the most popular of these new NoSQL options are document databases like MongoDB. MongoDB offers the flexibility to vary fields from document to document and change structure over time. Document databases typically store data in JSON-like documents, making it easy to map to objects in application code.

As the scale of NoSQL deployments in some organizations has rapidly grown, it has become increasingly important to have access to enterprise-grade tools to support modeling and management of NoSQL databases and to incorporate such databases into the broader enterprise data modeling and governance fold.

While document databases, key-value databases, graph databases and other types of NoSQL databases have added valuable options for developers to address various challenges posed by the “three Vs,” they did so largely by compromising consistency in favor of availability and speed, instead offering “eventual consistency.” Consequently, most NoSQL stores lack true ACID transactions, though there are exceptions, such as Aerospike and MarkLogic.

But some organizations are unwilling or unable to forgo consistency and transactional requirements, giving rise to a new class of modern relational database management systems (RDBMS) that aim to guarantee ACIDity while also providing the same level of scalability and performance offered by NoSQL databases.

NewSQL databases are typically designed to operate using a shared nothing architecture. VoltDB is one prominent example of this emerging class of ACID-compliant NewSQL RDBMS. The logical design for NewSQL database schemas is similar to traditional RDBMS schema design, and thus, they are well supported by popular enterprise-grade data modeling tools such as erwin DM.

Whatever mixture of databases your organization chooses to deploy for your OLTP requirements on premise and in the cloud – RDBMS, NoSQL and/or NewSQL – it’s as important as ever for data-driven organizations to be able to model their data and incorporate it into an overall architecture.

When it comes to organizations’ analytics requirements, including data that may be sourced from a wide range of NoSQL, NewSQL RDBMS and unstructured sources, leading organizations are adopting a variety of approaches, including a hybrid approach that many refer to as Managed Data Lakes.

Please join us next time for the fourth installment in our series: Data Modeling in a Jargon-filled World – Managed Data Lakes.

nosql

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Data Modeling in a Jargon-filled World – Big Data & MPP

Big Data & MPP

By now, you’ve likely heard a lot about Big Data. You may have even heard about “the three Vs” of Big Data. Originally defined by Gartner, “Big Data is “high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision-making, insight discovery and process optimization.”

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