Tag Archives: Hadoop

Rising DW Architecture Complexity

Rising DW Architecture Complexity

Rising DW Architecture Complexity

I was talking to an architect-customer last week at a company event and he was describing how his enterprise data warehouse architecture was getting much more complex after many years of relative calm and stability.  In the old days of yore, you had some data sources, a data warehouse (with single database), and some related edge systems.

The current trend is that new types of data and new types of physical storage are changing all of that.

When I got back from my trip I found a TDWI white paper by Philip Russom that describes the situation very well in a white paper detailing his research on this subject;  Evolving Data Warehouse Architectures in the Age of Big Data.

From an enterprise data architecture and management point of view, this is a very interesting paper.

  • First the DW architectures are getting complex because of all the new physical storage options available
    • Hadoop – very large scale and inexpensive
    • NoSQL DBMS – beyond tabular data
    • Columnar DBMS – very fast seek time
    • DW Appliances – very fast / very expensive
  • What is driving these changes is the rapidly-increasing complexity of data. Data volume has captured the imagination of the press, but it is really the rising complexity of the data types that is going to challenge architects.
  • But, here is what really jumped out at me. When they asked the people in their survey what are the important components of their data warehouse architecture, the answer came back; Standards and rules.  Specifically, they meant how data is modeled, how data quality metrics are created, metadata requirements, interfaces for data integration, etc.

The conclusion for me, from this part of the survey, was that business strategy is requiring more complex data for better analyses (example: realtime response or proactive recommendations) and business processes (example: advanced customer service).  This, in turn, is driving IT to look into more advanced technology to deal with different data types and different use cases for the data.  And finally, the way they are dealing with the exploding complexity was through standards, particularly data standards.  If you are dealing with increasing complexity and have to do it better, faster and cheaper, they only way you are going to survive is by standardizing as much as reasonably makes sense.  But, not a bit more.

If you think about it, it is good advice.  Get your data standards in place first.  It is the best way to manage the data and technology complexity.  …And a chance to be the driver rather than the driven.

I highly recommend reading this white paper.  There is far more in it than I can cover here. There is also a Philip Russom webinar on DW Architecture that I recommend.

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There are Three Kinds of Lies: Lies, Damned lies, and Data

Lies, Damned lies, and Data

Lies, Damned lies, and Data

The phrase Benjamin Disraeli used in the 19th century was: There are three kinds of lies: lies, damned lies, and statistics.

Not so long ago, Google created a Web site to figure out just how many people had influenza. How they did this was by tracking “flu-related search queries”, “location of the query,” and applied it to an estimation algorithm. According to the website, at the flu season’s peak in January, nearly 11 percent of the United States population may have influenza. This means that nearly 44 million of us will have had the flu or flu-like symptoms. In its weekly report the Centers for Disease Control and Prevention put this at 5.6%, which means that less than 23 million of us actually went to the doctor’s office to be tested for flu or to get a flu-shot.

Now, imagine if I were a drug manufacturer. There is a theory about what went wrong. The problems may be due to widespread media coverage of this year’s flu season. Then add social media, which helped news of the flu spread quicker than the virus itself. In other words, the algorithm is looking only at the numbers, not at the context of the search results.

In today’s digitally connected world, data is everywhere: in our phones, search queries, friendships, dating profiles, cars, food, and reading habits. Almost everything we touch is part of a larger data set. The people and companies that interpret the data may fail to apply background and outside conditions to the numbers they capture.

Now, while we build our big data repositories, we have to spend some time to explain how we collected the data and under what context.

Twitter @bigdatabeat

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Posted in Big Data, Cloud Data Management, Data Governance, Data Transformation, Data Warehousing, Hadoop | Tagged , , , , | Leave a comment

How to Get the Biggest Returns from Your Hadoop and Big Data Investments in 2015

Big Data2014 was the year that Big Data went mainstream from conversations asking “What is Big Data?” to “How do we harness the power of Big Data to solve real business problems”. It seemed like everyone jumped on the Big Data band wagon from new software start-ups offering the “next generation” predictive analytic applications to traditional database, data quality, business intelligence, and data integration vendors, all calling themselves Big Data providers. The truth is, they all play a role in this Big Data movement.

Earlier in 2014, Wikibon estimated the Big Data market is currently on pace to top $50 billion in 2017, which translates to a 38% compound annual growth rate over the six year period from 2011 (the first year Wikibon sized the Big Data market) to 2017. Most of the excitement around Big Data has been around Hadoop as early adopters who experimented with open source versions quickly grew to adopt enterprise-class solutions from companies like Cloudera™, HortonWorks™, MapR™, and Amazon’s RedShift™ to address real-world business problems including: (more…)

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Data Visibility From the Source to Hadoop and Beyond with Cloudera and Informatica Integration

Data Visibility From the Source to Hadoop

Data Visibility From the Source to Hadoop

This is a guest post by Amr Awadallah, Founder, CTO at Cloudera, Inc.

It takes a village to build mainstream big data solutions. We often get so caught up in Hadoop use cases and customer successes that sometimes we don’t talk enough about the innovative partner technologies and integrations that enable our customers to put the enterprise data hub at the core of their data architecture and innovate with confidence. Cloudera and Informatica have been working together to integrate our products to enable new levels of productivity and lower deployment and production risk.

Going from Hadoop to an enterprise data hub, means a number of things. It means that you recognize the business value of capturing and leveraging all your data for exploration and analytics. It means you’re ready to make the move from Hadoop pilot project to production. And it means your data is important enough that it’s worth securing and making data pipelines visible. It’s the visibility layer, and in particular, the unique integration between Cloudera Navigator and Informatica that I want to focus on in this post.

The era of big data has ushered in increased regulations in a number of industries – banking, retail, healthcare, energy – most of which deal in how data is managed throughout its lifecycle. Cloudera Navigator is the only native end-to-end solution for governance in Hadoop. It provides visibility for analysts to explore data in Hadoop, and enables administrators and managers to maintain a full audit history for HDFS, HBase, Hive, Impala, Spark and Sentry then run reports on data access for auditing and compliance.The integration of Informatica Metadata Manager in the Big Data Edition and Cloudera Navigator extends this level of visibility and governance beyond the enterprise data hub.

Hadoop
Today, only Informatica and Cloudera provide end-to-end data lineage from source systems through Hadoop, and into BI/analytic and data warehouse systems. And you can view it from a single pane within Informatica.

This is important because Hadoop, and the enterprise data hub in particular, doesn’t function in a silo. It’s an integrated part of a larger enterprise-wide data management architecture. The better the insight into where data originated, where it traveled, who had access to it and what they did with it, the greater our ability to report and audit. No other combination of technologies provides this level of audit granularity.

But more so than that, the visibility Cloudera and Informatica provides our joint customers with the ability to confidently stand up an enterprise data hub as a part of their production enterprise infrastructure because they can verify the integrity of the data that undergirds their analytics. I encourage you to check out a demo of the Informatica-Cloudera Navigator integration at this link: http://infa.media/1uBpPbT

You can also check out a demo and learn a little more about Cloudera Navigator  and the Informatica integration in the recorded  TechTalk hosted by Informatica at this link:

http://www.informatica.com/us/company/informatica-talks/?commid=133311

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Pour Some Schema On Me: The Secret Behind Every Enterprise Information Lake

Schema and the Enterprise Information Lake

Schema and the Enterprise Information Lake

Has there ever been a more exciting time in the world of data management?  With exponentially faster computing resources and exponentially cheaper storage, emerging frameworks like Hadoop are introducing new ways to capture, process, and analyze data.  Enterprises can leverage these new capabilities to become more efficient, competitive, and responsive to their customers.

Data warehousing systems remain the de facto standard for high performance reporting and business intelligence, and there is no sign that will change soon.  But Hadoop now offers an opportunity to lower costs by transferring infrequently used data and data preparation workloads off of the data warehouse and process entirely new sources of data coming from the explosion of industrial and personal devices.  This is motivating interest in new concepts like the “data lake” as adjunct environments to traditional data warehousing systems.

Now, let’s be real.  Between the evolutionary opportunity of preparing data more cost effectively and the revolutionary opportunity of analyzing new sources of data, the latter just sounds cooler.  This revolutionary opportunity is what has spurred the growth of new roles like data scientists and new tools for self-service visualization.  In the revolutionary world of pervasive analytics, data scientists have the ability to use Hadoop as a low cost and transient sandbox for data.  Data scientists can perform exploratory data analysis by quickly dumping data from a variety of sources into a schema-on-read platform and by iterating dumps as new data comes in.  SQL-on-Hadoop technologies like Cloudera Impala, Hortonworks Stinger, Apache Drill, and Pivotal HAWQ enable agile and iterative SQL-like queries on datasets, while new analysis tools like Tableau enable self-service visualization.  We are merely in the early phases of the revolutionary opportunity of big data.

But while the revolutionary opportunity is exciting, there’s an equally compelling opportunity for enterprises to modernize their existing data environment.  Enterprises cannot rely on an iterative dump methodology for managing operational data pipelines.  Unmanaged “data swamps” are simply unpractical for business operations.  For an operational data pipeline, the Hadoop environment must be a clean, consistent, and compliant system of record for serving analytical systems.  Loading enterprise data into Hadoop instead of a relational data warehouse does not eliminate the need to prepare it.

Now I have a secret to share with you:  nearly every enterprise adopting Hadoop today to modernize their data environment has processes, standards, tools, and people dedicated to data profiling, data cleansing, data refinement, data enrichment, and data validation.  In the world of enterprise big data, schemas and metadata still matter.

SchemaI’ll share some examples with you.  I attended a customer panel at Strata + Hadoop World in October.  One of the participants was the analytics program lead at a large software company whose team was responsible for data preparation.  He described how they ingest data from heterogeneous data sources by mandating a standardized schema for everything that lands in the Hadoop data lake.  Once the data lands, his team profiles, cleans, refines, enriches, and validates the data so that business analysts have access to high quality information.  Another data executive described how inbound data teams are required to convert data into Avro before storing the data in the data lake.  (Avro is an emerging data format alongside other new formats like ORC, Parquet, and JSON).  One data engineer from one of the largest consumer internet companies in the world described the schema review committee that had been set up to govern changes to their data schemas.  The final participant was an enterprise architect from one of the world’s largest telecom providers who described how their data schema was critical for maintaining compliance with privacy requirements since data had to be masked before it could be made available to analysts.

Let me be clear – these companies are not just bringing in CRM and ERP data into Hadoop.  These organizations are ingesting patient sensor data, log files, event data, clickstream data, and in every case, data preparation was the first task at hand.

I recently talked to a large financial services customer who proposed a unique architecture for their Hadoop deployment.  They wanted to empower line of business users to be creative in discovering revolutionary opportunities while also evolving their existing data environment.  They decided to allow line of businesses to set up sandbox data lakes on local Hadoop clusters for use by small teams of data scientists.  Then, once a subset of data was profiled, cleansed, refined, enriched, and validated, it would be loaded into a larger Hadoop cluster functioning as an enterprise information lake.  Unlike the sandbox data lakes, the enterprise information lake was clean, consistent, and compliant.  Data stewards of the enterprise information lake could govern metadata and ensure data lineage tracking from source systems to sandbox to enterprise information lakes to destination systems.  Enterprise information lakes balance the quality of a data warehouse with the cost-effective scalability of Hadoop.

Building enterprise information lakes out of data lakes is simple and fast with tools that can port data pipeline mappings from traditional architectures to Hadoop.  With visual development interfaces and native execution on Hadoop, enterprises can accelerate their adoption of Hadoop for operational data pipelines.

No one described the opportunity of enterprise information lakes better at Strata + Hadoop World than a data executive from a large healthcare provider who said, “While big data is exciting, equally exciting is complete data…we are data rich and information poor today.”  Schemas and metadata still matter more than ever, and with the help of leading data integration and preparation tools like Informatica, enterprises have a path to unleashing information riches.  To learn more, check out this Big Data Workbook

The Big Data Workbook

The Big Data Workbook

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Not Just For Play, Western Union Puts Hadoop to Work

Not Just For Play, Western Union Puts Hadoop to Work

Put Hadoop to Work

Everyone’s talking about Hadoop for empowering analysts to quickly experiment, discover, and predict new insights.  But Hadoop isn’t just for play.  Leading enterprises like Western Union are putting Hadoop to work on their most mission-critical data pipelines.  Last week at Strata + Hadoop World, we had a chance to hear how Western Union uses Cloudera’s Hadoop-based enterprise data hub and Informatica to deliver faster, simpler, and cleaner data pipelines.

Western Union, a multi-billion dollar global financial services and communications company, data is recognized as their core asset.  Like many other financial services firms, Western Union thrives on data for both harvesting new business opportunities and managing its internal operations.  And like many other enterprises, Western Union isn’t just ingesting data from relational data sources.  They are mining a number of new information-rich sources like clickstream data and log data.  With Western Union’s scale and speed demands, the data pipeline just has to work so they can optimize customer experience across multiple channels (e.g. retail, online, mobile, etc.) to grow the business.

Let’s level set on how important scale and speed is to Western Union.  Western Union processes more than 29 financial transactions every second.  Analytical performance simply can’t be the bottleneck for extracting insights from this blazing velocity of data.  So to maximize the performance of their data warehouse appliance, Western Union offloaded data quality and data integration workloads onto a Cloudera Hadoop cluster.  Using the Informatica Big Data Edition, Western Union capitalized on the performance and scalability of Hadoop while unleashing the productivity of their Informatica developers.

Informatica Big Data Edition enables data driven organizations to profile, parse, transform, and cleanse data on Hadoop with a simple visual development environment, prebuilt transformations, and reusable business rules.  So instead of hand coding one-off scripts, developers can easily create mappings without worrying about the underlying execution platform.  Raw data can be easily loaded into Hadoop using Informatica Data Replication and Informatica’s suite of PowerExchange connectors.  After the data is prepared, it can be loaded into a data warehouse appliance for supporting high performance analysis.  It’s a win-win solution for both data managers and data consumers.  Using Hadoop and Informatica, the right workloads are processed by the right platforms so that the right people get the right data at the right time.

Using Informatica’s Big Data solutions, Western Union is transforming the economics of data delivery, enabling data consumers to create safer and more personalized experiences for Western Union’s customers.  Learn how the Informatica Big Data Edition can help put Hadoop to work for you.  And download a free trial to get started today!

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Has Hadoop Crossed The Chasm? Thoughts About Strata 2014

Well, it’s been a little over a week since the Strata conference so I thought I should give some perspective on what I learned.  I think it was summed up at my first meeting, on the first morning of the conference. The meeting was with a financial services company who has significance experience with Hadoop. The first words out of their mouths were, “Hadoop is hard.” 

Later in the conference, after a Western Union representative spoke about their Hadoop deployment, they were mobbed by end user questions and comments. The audience was thrilled to hear about an actual operational deployment: Not just a sandbox deployment, but an actual operational Hadoop deployment from a company that is over 160 years old.

The market is crossing the chasm from early adopters who love to hand code (and the macho culture of proving they can do the hard stuff) to more mainstream companies that want to use technology to solve real problems. These mainstream companies aren’t afraid to admit that it is still hard. For the early adopters, nothing is ever hard. They love hard. But the mainstream market doesn’t view it that way.  They don’t want to mess around in the bowels of enabling technology.  They want to use the technology to solve real problems.  The comment from the financial services company represents the perspective of the vast majority of organizations. It is a sign Hadoop is hitting the mainstream market.

More proof we have moved to a new phase?  Cloudera announced they were going from shipping six versions a year down to just three.  I have been saying for awhile that we will know that Hadoop is real when the distribution vendors stop shipping every 2 months and go to a more typical enterprise software release schedule.  It isn’t that Hadoop engineering efforts have slowed down.  It is still evolving very rapidly.  It is just that real customers are telling the Hadoop suppliers that they won’t upgrade as fast because they have real business projects running and they can’t do it.  So for those of you who are disappointed by the “slow down,” don’t be.  To me, this is news that Hadoop is reaching critical mass.

Technology is closing the gap to allow organizations to use Hadoop as a platform without having to actually have an army of Hadoop experts.  That is what Informatica does for data parsing, data integration,  data quality and data lineage (recent product announcement).  In fact, the number one demo at the Informatica booth at Strata was the demonstration of “end to end” data lineage for data, going from the original source all the way to how it was loaded and then transformed within Hadoop.  This is purely an enterprise-class capability that becomes more interesting and important when you actually go into true production.

Informatica’s goal is to hide the complexity of Hadoop so companies can get on with the work of using the platform with the skills they already have in house.  And from what I saw from all of the start-up companies that were doing similar things for data exploration and analytics and all the talk around the need for governance, we are finally hitting the early majority of the market.  So, for those of you who still drop down to the underlying UNIX OS that powers a Mac, the rest of us will keep using the GUI.   To the extent that there are “fit for purpose” GUIs on top of Hadoop, the technology will get used by a much larger market.

So congratulations Hadoop, you have officially crossed the chasm!

P.S. See me on theCUBE talking about a similar topic at: youtu.be/oC0_5u_0h2Q

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Fast and Fasterer: Screaming Streaming Data on Hadoop

Hadoop

Guest Post by Dale Kim

This is a guest blog post, written by Dale Kim, Director of Product Marketing at MapR Technologies.

Recent published research shows that “faster” is better than “slower.” The point, ladies and gentlemen, is that speed, for lack of a better word, is good. But granted, you won’t always have the need for speed. My Lamborghini is handy when I need to elude the Bakersfield fuzz on I-5, but it does nothing for my Costco trips. There, I go with capacity and haul home my 30-gallon tubs of ketchup with my Ford F150. (Note: this is a fictitious example, I don’t actually own an F150.)

But if speed is critical, like in your data streaming application, then Informatica Vibe Data Stream and the MapR Distribution including Apache™ Hadoop® are the technologies to use together. But since Vibe Data Stream works with any Hadoop distribution, my discussion here is more broadly applicable. I first discussed this topic earlier this year during my presentation at Informatica World 2014. In that talk, I also briefly described architectures that include streaming components, like the Lambda Architecture and enterprise data hubs. I recommend that any enterprise architect should become familiar with these high-level architectures.

Data streaming deals with a continuous flow of data, often at a fast rate. As you might’ve suspected by now, Vibe Data Stream, based on the Informatica Ultra Messaging technology, is great for that. With its roots in high speed trading in capital markets, Ultra Messaging quickly and reliably gets high value data from point A to point B. Vibe Data Stream adds management features to make it consumable by the rest of us, beyond stock trading. Not surprisingly, Vibe Data Stream can be used anywhere you need to quickly and reliably deliver data (just don’t use it for sharing your cat photos, please), and that’s what I discussed at Informatica World. Let me discuss two examples I gave.

Large Query Support. Let’s first look at “large queries.” I don’t mean the stuff you type on search engines, which are typically no more than 20 characters. I’m referring to an environment where the query is a huge block of data. For example, what if I have an image of an unidentified face, and I want to send it to a remote facial recognition service and immediately get the identity? The image would be the query, the facial recognition system could be run on Hadoop for fast divide-and-conquer processing, and the result would be the person’s name. There are many similar use cases that could leverage a high speed, reliable data delivery system along with a fast processing platform, to get immediate answers to a data-heavy question.

Data Warehouse Onload. For another example, we turn to our old friend the data warehouse. If you’ve been following all the industry talk about data warehouse optimization, you know pumping high speed data directly into your data warehouse is not an efficient use of your high value system. So instead, pipe your fast data streams into Hadoop, run some complex aggregations, then load that processed data into your warehouse. And you might consider freeing up large processing jobs from your data warehouse onto Hadoop. As you process and aggregate that data, you create a data flow cycle where you return enriched data back to the warehouse. This gives your end users efficient analysis on comprehensive data sets.

Hopefully this stirs up ideas on how you might deploy high speed streaming in your enterprise architecture. Expect to see many new stories of interesting streaming applications in the coming months and years, especially with the anticipated proliferation of internet-of-things and sensor data.

To learn more about Vibe Data Stream you can find it on the Informatica Marketplace .


 

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Informatica’s Hadoop Connectivity Reaches for the Clouds

The Informatica Cloud team has been busy updating connectivity to Hadoop using the Cloud Connector SDK.  Updated connectors are available now for Cloudera and Hortonworks and new connectivity has been added for MapR, Pivotal HD and Amazon EMR (Elastic Map Reduce).

Informatica Cloud’s Hadoop connectivity brings a new level of ease of use to Hadoop data loading and integration.  Informatica Cloud provides a quick way to load data from popular on premise data sources and apps such as SAP and Oracle E-Business, as well as SaaS apps, such as Salesforce.com, NetSuite, and Workday, into Hadoop clusters for pilots and POCs.  Less technical users are empowered to contribute to enterprise data lakes through the easy-to-use Informatica Cloud web user interface.

Hadoop

Informatica Cloud’s rich connectivity to a multitude of SaaS apps can now be leveraged with Hadoop.  Data from SaaS apps for CRM, ERP and other lines of business are becoming increasingly important to enterprises. Bringing this data into Hadoop for analytics is now easier than ever.

Users of Amazon Web Services (AWS) can leverage Informatica Cloud to load data from SaaS apps and on premise sources into EMR directly.  Combined with connectivity to Amazon Redshift, Informatica Cloud can be used to move data into EMR for processing and then onto Redshift for analytics.

Self service data loading and basic integration can be done by less technical users through Informatica Cloud’s drag and drop web-based user interface.  This enables more of the team to contribute to and collaborate on data lakes without having to learn Hadoop.

Bringing the cloud and Big Data together to put the potential of data to work – that’s the power of Informatica in action.

Free trials of the Informatica Cloud Connector for Hadoop are available here: http://www.informaticacloud.com/connectivity/hadoop-connector.html

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