In the past, companies turned to data warehouses to manage, store, and process collected data. But the advent of Big Data strained these systems, pushed them to capacity, and drove up storage costs. As a result, some companies started moving their data into a new type of repository called a data lake.
A data lake structure tends to offer numerous advantages over other types of data repositories, such as data warehouses or data marts, in part due to its ability to store any type of data—internal, external, structured, or unstructured. Because of the lack of structure and increased flexibility in a data lake, it’s relatively easy to make changes to the repository’s models and queries as well as reconfigure the structure based on changing business needs.
In addition to the structural benefits, a data lake tends to improve accessibility and data democratization. Although data scientists are usually the primary users of data lakes, the repository makes it possible for anyone to efficiently and quickly extract insights from enterprise data. This type of accessibility supports iterative exploration and makes data lakes a perfect contender to find answers to problems that are less structured and require flexible solutions.
What more could you ask for in a data depository?
Now that you’ve decided a data lake is right for you and your business, it’s time to find out how to get started. In this article, we will discuss best practices for setting up a data lake and how to leverage data integration tools to ensure long-term success.
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How to Get Started with Data Lakes
In order to build a data lake that supports your business goals, you must first answer several fundamental questions to identify your organizational needs:
- Where does your data live?
- What types of data do you have?
- What’s happening to your data?
- Is your data accurate and secure?
- How can you avoid getting locked into any one technology or vendor?
- How will you be able to leverage future industry innovations?
In addition to understanding the state of your data, you will also need to consider who will be accessing your data and how they will get to it, since the primary benefit of a data lake is to make data easily accessible.
Once these factors are assessed and you’ve established your ideal data management strategy, you’re ready to create a data repository that will support your current requirements and scale to meet your future data storage needs.
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5 Steps to Data Lake Migration
With the rise in data lake and management solutions, it may seem tempting to purchase a tool off the shelf and call it a day. However, in order to establish a successful storage and management system, the following strategic best practices need to be followed.
1) Scale for tomorrow’s data volumes
The amount of data available is vast, and it’s only growing by the day. You’ll need to consider how your data lake will handle current as well as future data projects. That means ensuring you have enough developers, as well as processes in place, to manage, cleanse, and govern hundreds or thousands of new data sources efficiently and cost-effectively, without affecting performance.
2) Focus on business outcomes
You can’t transform your enterprise if you don’t understand what’s most important to the business. Understanding the organization’s core business initiatives is the key to identifying the questions, use cases, analytics, data, and underlying architecture and technology requirements for your data lake.
3) Expand the data team
Data quality is increasingly becoming a company-wide strategic priority involving individuals from different departments, rather than merely the IT team. With bad data often impacting business analysts, involving business users in your data quality process makes sense. Business analysts have the domain knowledge and skills to choose the right data for business needs, and by providing them with self-service access, you help ensure your data lake fulfills some of its key objectives.
4) Future-proof your infrastructure
Business needs are constantly changing, so your data lake will likely need to run on other platforms. Since different teams within the same organization often use different cloud providers based on their needs and resources, most companies operate in a multi-cloud infrastructure.
If this is the case in your organization, you’ll need to make sure your data infrastructure can handle that by opting for a flexible strategy that allows you to maintain agility as your technology choices change. A data vault methodology that gives you the flexibility to continuously onboard new types of data is often a sound approach.
5) Create a data governance strategy
Don’t wait until after your data lake is built to think about data quality. Having a well-crafted data governance strategy in place from the start is a fundamental practice for any big data project, helping to ensure consistent, common processes and responsibilities. Start by identifying business drivers for data that needs to be carefully controlled and the benefits expected from this effort. This strategy will be the basis of your data governance framework.
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How to Find the Best Data Integration Tool
Transitioning to a data lake can be complicated, but a data integration tool can help overcome most of the challenges you’re likely to encounter. When choosing a solution, look for one that can support every step of enterprise data management from data ingestion to data sharing. A data management tool should:
- Connect to unlimited data sources and allow you to add new sources easily
- Process data in a high-performance and secure fashion
- Process batch and real-time data at any speed
- Include built-in machine learning and data quality tools
- Include built-in data governance, metadata management, and data lineage tracking
- Offer self-service tools accessible to everyone from business users to skilled data scientists
- Run on any cloud or on-site platform
- Include built-in data vault capabilities and services
Finding a tool that hits all of these checkpoints will not only assist in a successful data lake setup, it will help you easily and efficiently maintain your data lake in a way that works best for your business.
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Lenovo Drives Revenue by 11% with a Cloud Data Lake
Lenovo, one of the world’s largest PC vendors, analyzes more than 22 billion transactions of structured and unstructured data annually. in order to achieve a 360-degree view of each of its millions of customers worldwide. With all this data at its fingertips, Lenovo struggled with quickly transforming rows of customer information into real business insights that could be applied in creating innovative new products. This challenge drove Lenovo to partner with Talend in order to build an agile cloud data lake that supports real-time predictive analytics.
By combining all available data into one single repository, Lenovo’s analytics team has access to data from more than 60 sources—furthering its quest to enhance the customer experience and differentiate the company from the competition. Measurable outcomes attributed to the cloud data lake project include: driving up revenue per unit by 11 percent, reducing operational costs by over $1 million within one year while more than doubling productivity, and cutting reporting and process times from hours to minutes.
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Many other organizations are finding that moving to a cloud data lake is the right choice to harness the power of their big data. When it is no longer a question of whether or not you need a data lake, but which solution to deploy. Talend Cloud provides a complete platform for turning raw data into valuable insights.
The Talend solution follows a proven methodology and open standards approach that eliminates many of the obstacles typically encountered in data lake deployments. By reducing hand coding, it solves portability and maintenance problems. In addition, its advanced platform enables routine tasks to be automated so developers can focus on higher-value work such as machine learning.
Ready for an efficient data management structure? Start building a data lake that works for your business KPIs with a free trial of Talend Cloud Integration.