Tag Archives: ETL
2014 was a pivotal turning point for Informatica as our investments in Hadoop and efforts to innovate in big data gathered momentum and became a core part of Informatica’s business. Our Hadoop related big data revenue growth was in the ballpark of leading Hadoop startups – more than doubling over 2013.
In 2014, Informatica reached about 100 enterprise customers of our big data products with an increasing number going into production with Informatica together with Hadoop and other big data technologies. Informatica’s big data Hadoop customers include companies in financial services, insurance, telcommunications, technology, energy, life sciences, healthcare and business services. These innovative companies are leveraging Informatica to accelerate their time to production and drive greater value from their big data investments.
These customers are in-production or implementing a wide range of use cases leveraging Informatica’s great data pipeline capabilities to better put the scale, efficiency and flexibility of Hadoop to work. Many Hadoop customers start by optimizing their data warehouse environments by moving data storage, profiling, integration and cleansing to Hadoop in order to free up capacity in their traditional analytics data warehousing systems. Customers that are further along in their big data journeys have expanded to use Informatica on Hadoop for exploratory analytics of new data types, 360 degree customer analytics, fraud detection, predictive maintenance, and analysis of massive amounts of Internet of Things machine data for optimization of energy exploration, manufacturing processes, network data, security and other large scale systems initiatives.
2014 was not just a year of market momentum for Informatica, but also one of new product development innovations. We shipped enhanced functionality for entity matching and relationship building at Hadoop scale (a key part of Master Data Management), end-to-end data lineage through Hadoop, as well as high performance real-time streaming of data into Hadoop. We also launched connectors to NoSQL and analytics databases including Datastax Cassandra, MongoDB and Amazon Redshift. Informatica advanced our capabilities to curate great data for self-serve analytics with a connector to output Tableau’s data format and launched our self-service data preparation solution, Informatica Rev.
Customers can now quickly try out Informatica on Hadoop by downloading the free trials for the Big Data Edition and Vibe Data Stream that we launched in 2014. Now that Informatica supports all five of the leading Hadoop distributions, customers can build their data pipelines on Informatica with confidence that no matter how the underlying Hadoop technologies evolve, their Informatica mappings will run. Informatica provides highly scalable data processing engines that run natively in Hadoop and leverage the best of open source innovations such as YARN, MapReduce, and more. Abstracting data pipeline mappings from the underlying Hadoop technologies combined with visual tools enabling team collaboration empowers large organizations to put Hadoop into production with confidence.
As we look ahead into 2015, we have ambitious plans to continue to expand and evolve our product capabilities with enhanced productivity to help customers rapidly get more value from their data in Hadoop. Stay tuned for announcements throughout the year.
Try some of Informatica’s products for Hadoop on the Informatica Marketplace here.
A Data Lake is a simple concept. They are a catchment area for data entering the organization. In the past, most businesses didn’t need to organize such a data store because almost all data was internal. It traveled via traditional ETL mechanisms from transactional systems to a data warehouse and then was sprayed around the business, as required.
When a good deal of data comes from external sources, or even from internal sources like log files, which never previously made it into the data warehouse, there is a need for an “operational data store.” This has definitely become the premier application for Hadoop and it makes perfect sense to me that such technology be used for a data catchment area. The neat thing about Hadoop for this application is that:
- It scales out “as far as the eye can see,” so there’s no likelihood of it being unable to manage the data volumes even when they grow beyond the petabyte level.
- It is a key-value store, which means that you don’t need to expend much effort in modeling data when you decide to accommodate a new data source. You just define a key and define the metadata at leisure.
- The cost of the software and the storage is very low.
So let’s imagine that we have a need for a data catchment area, because we have decided to collect data from log-files, mobile devices, social networks, from public data sources, or whatever. So let us also imagine that we have implemented Hadoop and some of its useful components and we have begun to collect data.
Is it reasonable to describe this as a data lake?
A Hadoop implementation should not be a set of servers randomly placed at the confluence of various data flows. The placement needs to be carefully considered and if the implementation is to resemble a “data lake” in any way, then it must be a well-engineered man-made lake. Since the data doesn’t just sit there until it evaporates but eventually flows to various applications, we should think of this as a “data reservoir” rather than a “data lake.”
There is no point in arranging all that data neatly along the aisles because when we get it, we may not know what we want to do with it at the time we get it. We should organize the data when we know that.
Another reason we should think of this as more like a reservoir than a lake is that we might like to purify the data a little before sending it down the pipes to applications or users that want to use it.
Today, 80% of the efforts in Big Data projects are related to extracting, transforming and loading data (ETL). Hortonworks and Informatica have teamed-up to leverage the power of Informatica Big Data Edition to use their existing skills to improve the efficiency of these operations and better leverage their resources in a modern data architecture. (MDA)
Next Generation Data Management
The Hortonworks Data Platform and Informatica BDE enable organizations to optimize their ETL workloads with long-term storage and processing at scale in Apache Hadoop. With Hortonworks and Informatica, you can:
• Leverage all internal and external data to achieve the full predictive power that drives the success of modern data-driven businesses.
• Optimize the entire big data supply chain on Hadoop, turning data into actionable information to drive business value.
Imagine a world where you would have access to your most strategic data in a timely fashion, no matter how old the data is, where it is stored, or under what format. By leveraging Hadoop’s power of distributed processing, organizations can lower costs of data storage and processing and support large data distribution with high through put and concurrency.
Overall, the alignment between business and IT grows. The Big Data solution based on Informatica and Hortonworks allows for a complete data pipeline to ingest, parse, integrate, cleanse, and prepare data for analysis natively on Hadoop thereby increasing developer productivity by 5x over hand-coding.
Where Do We Go From Here?
At the end of the day, Big Data is not about the technology. It is about the deep business and social transformation every organization will go through. The possibilities to make more informed decisions, identify patterns, proactively address fraud and threats, and predict pretty much anything are endless.
This transformation will happen as the technology is adopted and leveraged by more and more business users. We are already seeing the transition from 20-node clusters to 100-node clusters and from a handful of technology-savvy users relying on Hadoop to hundreds of business users. Informatica and Hortonworks are accelerating the delivery of actionable Big Data insights to business users by automating the entire data pipeline.
Try It For Yourself
On September 10, 2014, Informatica announced the 60-day trial version of the Informatica Big Data Edition into the Hortonworks Sandbox. This free trial enables you to download and test out the Big Data Edition on your notebook or spare computer and experience your own personal Modern Data Architecture (MDA).
If you happen to be at Strata this October 2014, please meet us at our booths: Informatica #352 and Hortonworks #117. Don’t forget to participate in our Passport Program and join our session at 5:45 pm ET on Thursday, October 16, 2014.
Managing the recovery and flow of data files throughout your enterprise is much like managing the flow of oil from well to refinery – a wide range of tasks must be carefully completed to ensure optimal resource recovery. If these tasks are not handled properly, or are not addressed in the correct order, valuable resources may be lost. When the process involves multiple pipelines, systems, and variables, managing the flow of data can be difficult.
Organizations have many options to automate the processes of gathering data, transferring files, and executing key IT jobs. These options include home-built scheduling solutions, system integrated schedulers, and enterprise schedulers. Enterprise schedulers, such as Skybot Scheduler, often offer the most control over the organization’s workflow, as they offer the ability to create schedules connecting various applications, systems, and platforms.
In this way, the enterprise scheduler facilitates the transfer of data into and out of Informatica PowerCenter and Informatica Cloud, and ensures that raw materials are refined into valuable resources.
Enterprise Scheduling Automates Your Workflow
Think of an enterprise scheduler as the pipeline bearing data from its source to the refinery. Rather than allowing jobs or processes to execute randomly or to sit idle, the enterprise scheduler automates your organization’s workflow, ensuring that tasks are executed under the appropriate conditions without the need for manual monitoring or the risk of data loss.
Skybot Scheduler addresses the most common pain points associated with data recovery, including:
- Scheduling dependencies: In order for PowerCenter or Cloud to complete the data gathering processes, other dependencies must be addressed. Information must be swept and updated, and files may need to be reformatted. Skybot Scheduler automates these tasks, keeping the data recovery process consistently moving forward.
- Reacting to key events: As with oil recovery, small details can derail the successful mining of data. Key events, such as directory changes, file arrivals, and evaluation requirements can lead to a clog in the pipeline. Skybot Scheduler maintains the flow of data by recognizing these key events and reacting to them automatically.
Choose the Best Pipeline Available
Skybot Scheduler is one of the most powerful enterprise scheduling solutions available today, and is the only enterprise scheduler integrated with PowerCenter and Cloud.
Capable of creating comprehensive cross-platform automation schedules, Skybot Scheduler manages the many steps in the process of extracting, transforming, and loading data. Skybot maintains the flow of data by recognizing directory changes and other key events, and reacting to them automatically.
In short, by managing your workflow, Skybot Scheduler increases the efficiency of ETL processes and reduces the potential of a costly error.
To learn more about the power of enterprise scheduling and the Skybot Scheduler check out this webinar: Improving Informatica ETL Processing with Enterprise Job Scheduling or download the Free Trial.
On a recent trip to a new city, someone said that the easiest way from the airport to the hotel was to use the Metro. I could speak the language, but reading it was another matter. I was surprised by how quickly I navigated to the hotel by following the Metro map. The Metro map is based on the successful design of the London Underground map.
Harry Beck was not a cartographer. He was an engineering draftsman. He started drawing a different type of map in his spare time. Beck believed that the passengers were not worried about the distance accuracy of the map. He reduced the map to straight lines and sharp angles, which produced a map closer to an electrical schematic diagram rather than a more common geographic map. The company that ran the London Underground was skeptical of Beck’s map since it was radically different and they had not commissioned the project. (more…)
This is the second installment of my multi-part blog series on “hitting the batch wall.” Well, it’s not so much about hitting the batch wall, but what you can do to avoid hitting the wall. Today’s topic is “throwing hardware” at the problem (a.k.a. hardware scaling). I’ll discuss the common approaches and the tradeoffs of hardware scaling with Informatica software.
Before I can begin to discuss hardware scaling, I start with this warning: faster hardware only improves the load window situation when it resolves a bottleneck. Data integration jobs are a lot like rush hour traffic, they can only run as fast as the slowest component. It doesn’t make any sense to buy a Ferrari if you will always be driving behind a garbage truck. In other words, if your ETL jobs are constrained by the source/target systems or I/O or even just memory, then faster/more CPUs will rarely improve the situation. Understand your bottlenecks before you start throwing hardware at them! (more…)
This year marks the 20th anniversary for Informatica. Twenty years of solving the problem of getting data from point A to point B, improving its quality, establishing a single view and managing it over its life-cycle. Yet after 20 years of innovation and leadership in the data integration market, when one would think the problem had been solved, all data had been extracted, transformed, cleansed and managed, it actually hasn’t — companies still need data integration. Why? Data is complicated business. And with data increasingly becoming central to business survival, organizations are constantly looking for ways to unlock new sources of it, use it as an unforeseen source of insight and do it all with greater agility and at lower cost. (more…)
In a recent webinar, Mark Smith, CEO at Ventana Research and David Lyle, vice president, Product Strategy at Informatica discussed: “Building the Business Case and Establishing the Fundamentals for Big Data Projects.” Mark pointed out that the second biggest barrier that impedes improving big data initiatives is that the “business case is not strong enough.” The first and third barriers respectively, were “lack of resources” and “no budget” which are also related to having a strong business case. In this context, Dave provided a simple formula from which to build the business case:
Return on Big Data = Value of Big Data / Cost of Big Data (more…)