Data Integration for Salesforce.com is no more a challenging task

Enterprises implement SalesForces so that teams can focus on customers and revenue, leaving the rest to automation.  But when companies try to extend that automation by integrating SalesForce with an ERP, the result too often is more headache than focus.  MIDAS an ETLE Tool (Extraction, Transformation, Loading and Enrichment) resolves the challenge by seamlessly integrating capabilities from Saleforce to and from SAP, EBS and other ERPs. Midas seamlessly integrates Saleforce.com with SAP, Oracle E-Business Suite and other ERPs.

Feature set

• Cloud based solution for invoking transformations and jobs remotely
• Broad connectivity and data delivery
• Hosting a Cloud solution with multi-tenancy capability
• Social connectors – Facebook, Twitter and LinkedIn
• Custom Connectors – SAP, Oracle E-Business Suite and Salesforce.com
• Analytics integration with Pentaho Reports, OBIEE and SAP BusinessObjects
• Powerful administration and management
• Data profiling and data quality
• Single interface to manage all the integration projects
• Flexible deployment options
• Bi-directional CRM-ERP integration

Key Benefits

• Designed for seamlessly integrating Salesforce.com with ERPs and other applications
• 300 plus Open – source connectors out of the box
• Transparent diagrammatic depiction of data transfer steps
• Removes custom coding for quick turn around
• Reduces integration and maintenance costs
• Improves data quality
• Shortens implementation time
• Easy installation and configuration
• Business continuity and application availability management
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PIG and Big Data – Processing Massive Data Volumes at High Speed

For most organizations, availability of data is not the challenge.  Rather, it’s handling, analyzing, and reporting on that data in a way that can be translated into effective decision-making.

PIG is an open source project intended to support ad-hoc analysis of very large data volumes. It allows us to process data collected from a myriad of sources such as relational databases, traditional data warehouses, unstructured internet data, machine-generated log data, and free-form text.

How does it process?

PIG is used to build complex jobs behind the scenes to spread the load across many servers and process massive quantities of data in an endlessly scalable parallel environment.

Unlike traditional BI tools that are used to report on structured data, PIG is a high level data flow language which creates step-by-step procedures on raw data to derive valuable insights. It offers major advantages in efficiency and flexibility to access different kinds of data.

What does PIG do?

PIG opens up the power of Map Reduce to the non-java community. The complexity of writing java programs can be avoided by creating simple procedural language abstraction over Map Reduce to expose a more Structured Query Language (SQL)-like interface for big data applications.

PIG provides common data processing operations for web search platforms like web log processing. PIG Latin is a language that follows a specific format in which data is read from the file system, a number operations are performed on the data (transforming it in one or more ways), and then the resulting relation is written back to the file system.

PIG scripts can use functions that you define for things such as parsing input data or formatting output data and even operators. UDFs (user defined functions) are written in the Java language and permit PIG to support custom processing. UDFs are the way to extend PIG into your particular application domain.

PIG allows rapid prototyping of algorithms for processing petabytes of data. It effectively addresses data analysis challenges such as traffic log analysis and user consumption patterns to find things like best-selling products.

Common Use Cases:

Mostly used for data pipelining which includes bringing in data feed, data cleansing, and data enhancements through transformations. A common example would be log files.

PIG is used for iterative data processing to allow time sensitive updates to a dataset. A common example is “Bulletin”, which involves constant inflow of small pieces of new data to replace the older feeds every few minutes.

Sailaja Bhagavatula specializes in SAP Business Objects and Hadoop for Bodhtree, a business analytics services company focused on helping customers get maximum value from their data.  Bodhtree not only implements the tools to enable processing and analysis of massive volumes of data, we also help business to ensure the questions being asked target key factors for long term growth.

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How BI for mobile devices on top of Big Data can transform your employees, customers, and enterprise



BI is going Mobile – in a ‘Big’ way.   Business decisions are not always made in the corner office or cubicle.  They are made on retail floors, in delivery trucks, on an ambulance, or at the laboratory.

Mobile BI delivers intelligence to where ever your team makes the critical decisions.   By delivering the right information to the right people at the right time, mobile BI accelerates response to real-time information and enables a more agile enterprise.

Big data

Big data is exponentially enhancing intelligence quotient of BI by better leveraging all data inside and outside an enterprise; this intelligence can empower business decisions in terms of both timing and reasoning

Combination of Mobile BI and Big Data

Now the combination of Mobile BI and Big Data further reduces the gap between data generation and business decision.

In a recent survey conducted by SAP, 70% of CIOs envisioned killer Big Data apps useful to their enterprises, but interestingly most of them chose not to reveal the idea as it would reduce the competitive advantage.

What types of mobile analytics on Big Data apps could these CIO’s consider so critical to a market advantage?  Possibilities include geospatial intelligence, behavioral intelligence, enhanced customer interaction, etc.

Sample Use case

For U.S. Xpress Inc., a trucking company based in Chattanooga, Tenn., the driver to move to Big Data analytics and real-time BI reporting was a desire to get more out of the large volumes of sensor data being collected from the company’s trucks. U.S. Xpress was looking to use the data to enable its fleet managers to “answer very specific, detailed questions” about trucking operations.

Phani K Reddy is a Big Data Architect with Bodhtree, a leader in Data Analytics, Business Intelligence, and Big Data services. Bodhtree provides Hadoop implementation and maintenance services as an end-to-end service to solve specific business challenges.

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Big Data – How does it impact an Organization?

Business Intelligence

“Data will grow to several petabytes and zeta bytes in the next few years;” “Corporations will be overwhelmed by the sheer volume of data;” “By 2015 databases will grow so large that companies will have to rent space on the moon to store them.” These are just a few common quotes we hear from renowned industry analysts, experts and CIOs surrounding the topic of Big Data.

But for many observers, there’s still a lot of gray area surrounding when to apply Big Data solutions.  How is Big Data different from traditional analysis tools?  How does it impact an organization? Just because there are several terabytes of data, does that make it Big Data? Does it really matter?

We can make a generic bold statement here: Data is everywhere! When you pause to think, you’ll see it’s true. The cars we drive, stores we shop at, the phone we use, the websites we read, the social media we so closely embrace, the TV programs we watch, the election polls that we follow – everything generates data and it is all stored somewhere.

When it comes to Big Data, people commonly mention the 4 Vs, but this only scratches the surface.  Traditionally, any large organization that implements ERP services has all these Vs associated with their everyday line of business.  Admittedly, this is primarily “structured” data, and it can be handled by a traditional RDBMS or an MPP database.  But in light of the other Vs, it technically has volume because everything is recorded in an ERP system; the incorrect and the correct entries alike form the millions and billions of rows.  It undoubtedly has value; otherwise corporations would not bother to collect it. It clearly is volatile because data does not come at regular intervals. It also has variety; data comes in from multiple sources, such as CRM systems, HR systems and modules that are implemented within the organization.

Even if you add unstructured data, can you really derive strategic value for your business? What is this unstructured data comprised of? What kind of metrics can we extract from this unstructured data?

More often than not, Big Data buzz induces a cool factor akin to what was once associated with owning an iPhone/iPad or, back in the day, having a free Facebook account. Sometimes a personal agenda is associated with Big Data implementations. Several managers and CIOs have vested interests in showing off a ‘cutting edge’ Big Data implementation.  We recommend organizations take a breather to make sure that the data problem they have qualifies as a Big Data problem.

…Stay tuned for a model CIOs can use to evaluate whether Big Data is the solution to their challenge…

Ajay Narayan is a presales solution architect for Bodhtree, a leader in data analytics and product engineering.  Bodhtree partners with SAP and Informatica to assist companies in leveraging enterprise data to gain a competitive edge in the market.  Bodhtree also offers the MIDAS product suite, which migrates, cleanses and integrates data between SalesForce, SAP and legacy platforms.

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