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Using Talend Big Data Platform to build a cloud data lake on the Microsoft Azure Cloud Platform, learn how a company was able to integrate and cleanse data from multiple sources and deliver real-time insights.


The process of extracting value from your data, especially in industries such as financial services, can be a challenge. There’s the sheer volume of data, which is on pace to double every two years.

Data comes from multiple sources, can be spread across your infrastructure or stuck in silos, and can be hard to integrate when it’s distributed across cloud and on-premises platforms. Increasing and changing regulations can hinder the process, too. As well, your customers demand self-service access in real time, and your teams want more insights, and faster.

In this eBook, learn why a cloud data lake you trust can help you scale the process of integrating data, assessing its quality, tracking lineage, and extracting value from data—with confidence.


Talend is a leading cloud and big data software provider for data-driven companies, offering a single platform for data integration, data management, and application integration use cases that delivers agile analytics across public, private, and hybrid clouds as well as on-premises environments.

In partnership with Microsoft, Talend provides fast development of Big Data ETL processing, cloud data lakes, cloud data warehousing, and real-time analytics projects on the Microsoft Azure Cloud Platform. This empowers companies to solve modern integration and analytics challenges by connecting businesscritical data and applications from on-premises systems, cloud, social, and mobile apps in real-time at a predictable price.

By combining the power of Talend and Microsoft Azure, many organizations have successfully modernized their cloud platforms for big data analytics. This white paper details use cases in the energy, food, beverage & brewing, and logistics industries, as well as the IT architectures that were used in the solutions.


Data privacy and protection are increasingly capturing the attention of business leaders, citizens, law enforcement agencies and governments. Data regulations, whose reach used to be limited to heavily regulated industries such as banking, insurance, healthcare or life sciences are now burgeoning across countries and apply to any business no matter its size or industry, highlighting the importance of a concept called data sovereignty. Data sovereignty refers to legislation that covers information that is subject to the laws of the country in which the information is located or stored. It impacts the protection of data and is affected by governmental regulations for data privacy, data storage, data processing, and data transfers across country boundaries. These laws are emerging as a key impediment to cloud-based storage of data, and they need to be fully understood and considered when information is created in one country but then moved to another country for analytics or processing. Data sovereignty regulations address multi-dimensional challenges across multiple subject areas (customer, employee, citizen, prospect, visitor, job seeker, vendor), emerging data types (internet of things, log files, biometrics), diverse jurisdictions and rapidly changing laws.


Talend and Transforming Data With Intelligence (TDWI) conducted a survey in September and October 2018. Respondents noted that adopting CDWs was critical to helping them achieve faster performance, lower their costs, and take advantage of cloud features. But there were a number of challenges associated with CDWs as well, like governing data and integrating it with different sources. It is clear that respondents need their CDWs to do more than store their data.

In this report, we’ll discuss CDW industry trends and best practices. We’ll also take an in-depth look at the additional feature functionality required by CDW users for today’s data management needs. For today’s IT decision makers, a CDW is an important first step to becoming a data-driven enterprise. But it’s only a first step.

This report helps shed light on how to use CDWs as a part of a comprehensive data management program.


We’ve entered the era of the information economy where data has become the most critical asset of every organization. Data-driven strategies are now a competitive imperative to succeed in every industry. To support business objectives such as revenue growth, profitability, and customer satisfaction, organizations are increasingly reliant on data to make decisions. Data-driven decision making is at the heart of your digital transformation initiatives.

But in order to provide the business with the data it needs to fuel digital transformation, organizations must solve two problems at the same time.

The data must be timely, because digital transformation is all about speed and accelerating time to market— whether that’s providing real-time answers for your business teams or delivering personalized customer experiences. However, most companies are behind the curve when it comes to delivering technology initiatives quickly.

But while speed is critical, it’s not enough. For data to enable effective decision-making and deliver remarkable customer experiences, organizations need data they can trust. This is also a major challenge for organizations. Being able to trust your data is about remaining on the right side of regulation and customer confidence, and it’s about having the right people using the right data to make the right decisions.


Poor data quality can be mitigated much more easily if caught before it is used — at its point of origin. If you
verify or standardize data at the point of entry, before it makes it into your back-end systems, we can say that it
costs about $1 to standardize it. If you cleanse that data later, going through the match and cleanse in all the
different places, then it would cost $10 in comparison to the first dollar in terms of time and effort expended.

And just leaving that bad quality data to sit in your system and continually give you degraded information to
make decisions on, or to send out to customers, or present to your company, would cost you $100 compared to the $1 it would’ve cost to actually deal with that data at the point of entry, before it gets in. The cost gets greater the longer bad data sits in the system.


It’s common to see specialized data quality tools requiring deep expertise for successful deployment. These tools are often complex and require in-depth training to be launched and used. Their User Interface is not suitable for everyone so only IT people can manage them.

While these tools can be powerful, if you have short term data quality priorities, you will miss your deadline. Don’t ask a rookie to pilot a jumbo jet. The flight instruments are obviously too sophisticated and it won’t be successful.

On the other hand, you will find simple and often robust apps that can be too siloed to be injected into a comprehensive data quality process. Even if they successfully focus on the business people with a simple
UI, they will miss the big part — collaborative data management.

And that’s precisely the challenge. Success relies not only in the tools and capabilities themselves, but in their ability to talk to each other. You therefore need to have a platform-based solution that shares, operates, and transfers data, actions, and models together.

That’s precisely what Talend provides.


Talend is a leading data integration and data management solution provider for datadriven companies. As an Advanced Technology Partner in the Amazon Web Services Partner Network, Talend provides fast development of Big Data, real-time analytics and ETL projects on Amazon Web Services, empowering companies to solve modern
integration challenges by connecting business-critical data and applications from onpremises systems, cloud applications, web, social, and mobile apps in days at a predictable price.

By combining the power of Talend and AWS, many customers were able to successfully transform their businesses. This paper describes use cases in the pharmaceutical and food and beverage industries, as well as the IT architectures that were used in the solutions.