Data Warehousing Fundamentals For It Professionals
OUR TAKE: This title was specifically written for professionals responsible for designing, implementing, or maintaining data warehousing systems. It is also relevant for those working in research and information management.
This practical Second Edition highlights the areas of data warehousing and business intelligence where high-impact technological progress has been made. Discussions on developments include data marts, real-time information delivery, data visualization, requirements gathering methods, multi-tier architecture, OLAP applications, Web clickstream analysis, data warehouse appliances, and data mining techniques. The book also contains review questions and exercises for each chapter, and is appropriate for self-study or classroom work.
Get Help From The Experts
The Google CloudProfessional Services organization offers consulting services to help you on your Google Cloudjourney. Contact PSO consultants, who can provide deep expertise to educate yourteam on best practices and guiding principles for a successful implementation.Services are delivered in the form of packages to help you plan, deploy, execute, andoptimize workloads.
Google Cloud also has a strong ecosystem of,from large global systems integrators to partners with a deep specialization ina particular area like machine learning. Partners have demonstrated customersuccess using Google Cloud and can accelerate your projects and improvebusiness outcomes. We recommend that enterprise customers engage partners tohelp plan and execute their Google Cloud implementation.
Leverage Read Access Geo Redundant Storage
One of the biggest benefits of cloud computing is the fact that you can replicate your business data and keep it as a backup in another Azure region. This helps your business application to remain highly available.
In the case of ADL Gen2, Azure recommends that you spin up a Geo Redundant Storage so that the data remains available in another Azure region. You should give your application read-access to the data in the second region so that in case of an outage, the application can keep on using secondary data storage as if nothing has happened.
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What Are Data Governance Best Practices
Best practices come from experience, so its always a good idea to look around and consider what other organizations have done when implementing and working with a data governance program. One of the best things you can do is start small and build your data governance program from there. This will help you test strategies and figure out what works best in your unique environment.
Data governance best practices are a set of recommendations that weve identified based on the successes weve seen with our customers. Our best practices can help ensure that your business gets the most out of its data governance program. Well discuss what we think are the six best practices for a data governance strategy below.
Some of these can be big-picture strategies, including clearly defining and communicating your organizations vision and goals for your data governance program and making sure to measure your progress in several different ways. Others can be more technical, such as regularly participating in enterprise data architecture reviews or emphasizing automation when it comes to data requests and permissions, workflows, and approval processes.
Embrace Devops And Explore Site Reliability Engineering
To increase agility and reduce time-to-market for apps and features, you needto break down silos between development, operations, networking, andsecurity teams. Doing so requires processes, culture, and tooling that aretogether referred to as DevOps.
Google Cloud provides a range of services to help you adopt DevOpspractices. Features include integrated,continuous-delivery tooling,rich monitoring capabilities through Cloud Monitoring, and strongsupport for open source tools. For more details, see Google Cloud’sDevOps solutions.
Site Reliability Engineering is a set of practices closely related to DevOps. These practices evolvedfrom the SRE team that manages Google’s production infrastructure. Whilecreating a dedicated SRE function is beyond the scope of many enterprises, we recommend that you study theSRE booksto learn practices that can help shape your operations strategy.
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Data Governance Don’t Drown In Your Data Lake
Recorded On: 04/14/2021
This webinar will cover a variety of lessons learned, best practices and innovations that our speakers have developed in their Data Governance Programs, Big Data Challenges, and Enterprise Data Management systems across one or many stakeholder organizations. Transit agencies and experts will provide the information you need to set your data governance program in the best possible direction. Speakers will share the steps of conducting an assessment and share with you a set of typical results from taking this action. You may learn how easy it is to organize the assessment and hear results that encourage you to take action at your own agency.
Moderator:
- Ahsan Baig, Chief Information Officer, AC Transit , Oakland, CA
Panelists:
- Manjit Sooch, Chair, APTA Research and Technology Committee Director of Systems and Software Development, AC Transit , Oakland, CA
- Karen A. Winger, AICP, CCTM, TDM-CP, Transit Division Director, Transportation, Gwinnett County Department of Transportation, Lawrenceville, GA
- Dr. Brendon Hemily, Ph.D., Senior Advisor, Transit Analytics Lab , University of Toronto, Toronto, ON
- Joseph Yawn, Transportation Technology Administrator, Mobility Services Group, Atlanta Regional Commission, Atlanta, GA
Key:
Control Access To Resources
You must authorize your developers and IT staff to consume Google Cloudresources. You can useIdentity and Access Management to grant granular access to specific Google Cloud resources andprevent unwanted access to other resources. Specifically, IAMenables you to control access by defining who has what access for which resource.
Rather than directly assigning permissions, you assign roles.IAMrolesare collections of permissions. For example, the BigQuery Data Viewerrole contains the permissions to list, read, and query BigQuery tables,but does not include permissions to create new tables or modify existing data.IAM provides many predefined roles to handle a wide range ofcommon use cases. It also enables you to create custom roles.
Use IAM to apply the security principle of least privilege, soyou grant only the necessary access to your resources. IAM is afundamental topic for enterprise organizations. For more information aboutidentity and access management, see the following resources:
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Business Benefits To Earn
There are multiple reasons why good data warehouse governance is a must, and it goes beyond the need for better data collection and management. Yes, having an efficient system for collecting and processing data allows the enterprise to benefit from lower data management costs in the long run, but the business implications are no less significant.
For starters, data can be fully integrated and processed holistically. Data relating to financial activities of the company, for instance, can be made more valuable when compounded with external data about market growth, competitors actions, and industry average values. For example, sales data can be analyzed in a deeper way within the context of market performance and changes.
The result is a healthier data-driven decision-making process, and one that encourages collaboration between departments. When a thorough analysis is performed, multiple aspects can be taken into account from the start. When deciding to expand the manufacturing line, for instance, market insights can be just as valuable as data from the sales and marketing teams.
There is also the possibility of increasing revenue from good data warehouse governance, both from the reduction of CAPEX and OPEX, and from the increase in revenue through the discovery of new opportunities. These are objectives that can be achieved through better data management and more accurate decision-making processes.
Opinions expressed by DZone contributors are their own.
Data: Lakes Or Swamps
An un-managed repository of large data sets can easily become a swamp of badly biased data sets.
To help manage this element of innovation, organizations need to govern their data lake. Raw structured and unstructured data trusted, secured, and governed will be kept in the lake for the necessary time period. This kind of organization is known as a Governed Data Lake.
For organizations that derive value from their data, including data about customers, employees, transactions, and other assets, governed data lakes create opportunities to identify, understand, share and confidently act upon information.
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Why Are Data Lakes Important
Because a data lake can rapidly ingest all types of new data while providing self-service access, exploration and visualization businesses can see and respond to new information faster. Plus, they have access to data they couldnt get in the past.
These new data types and sources are available for data discovery, proofs of concept, visualizations and advanced analytics. For example, a data lake is the most common data source for machine learning a technique thats often applied to log files, clickstream data from websites, social media content, streaming sensors and data emanating from other internet-connected devices.
Many businesses have long wished for the ability to perform discovery-oriented exploration, advanced analytics and reporting. A data lake quickly provides the necessary scale and diversity of data to do so. It can also be a consolidation point for both big data and traditional data, enabling analytical correlations across all data.
Although its typically used to store raw data, a lake can also store some of the intermediate or fully transformed, restructured or aggregated data produced by a data warehouse and its downstream processes. This is often done to reduce the time data scientists must spend on common data preparation tasks.
Check Out Some Related Content
Data lakes are formally included in many organizations’ data and analytics strategies today.
Ready to learn more about some related topics? In the box to the right, learn how data integration has evolved and check out our tips for building better data lakes. Discover why governance is essential, and get the latest on data tagging best practices. Or, read all about the ins and outs of cloud computing.
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Metrics And More Metrics
As with any goal, if you cannot measure it, you cannot reach it. When making any change, you should measure the baseline before to justify the results after. Collect those measurements early, and then consistently track each step along the way. You want your metrics to show overall changes over time and serve as checkpoints to ensure the processes are practical and effective.
Specify Your Project Structure
A project is required in order to use Google Cloud. AllGoogle Cloud resources, such as Compute Engine virtual machines andCloud Storage buckets, belong to a single project. For more information aboutprojects, see thePlatform overview.
You control the scope of your projects. A single project might contain multipleseparate apps, or conversely a single app might include several projects.Projects can contain resources spread across multiple regions and geographies.
A general recommendation is to have one project per application perenvironment. For example, if you have two applications, “app1” and “app2”,each with a development and production environment, you would have fourprojects: app1-dev, app1-prod, app2-dev, app2-prod. This isolates theenvironments from each other, so changes to the development project do notaccidentally impact production, and gives you better access control, sinceyou can grant all developers access to development projects butrestrict production access to your CI/CD pipeline.
The ideal project structure depends on your individual requirements, and mightevolve over time. When designing project structure, determine whether resourcesneed to be billed separately, what degree of isolation is required, and how theteams that manage the resources and apps are organized.
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Catalog The Data In Your Lakehouse
In order to implement a successful lakehouse strategy, its important for users to properly catalog new data as it enters your data lake, and continually curate it to ensure that it remains updated. The data catalog is an organized, comprehensive store of table metadata, including table and column descriptions, schema, data lineage information and more. It is the primary way that downstream consumers can discover what data is available, what it means, and how to make use of it. It should be available to users on a central platform or in a shared repository.
At the point of ingestion, data stewards should encourage users to tag new data sources or tables with information about them including business unit, project, owner, data quality level and so forth so that they can be sorted and discovered easily. In a perfect world, this ethos of annotation swells into a company-wide commitment to carefully tag new data. At the very least, data stewards can require any new commits to the data lake to be annotated and, over time, hope to cultivate a culture of collaborative curation, whereby tagging and classifying the data becomes a mutual imperative.
Data Owners Are Agreed
Data Owners should be approving whether the data they own is appropriate to be loaded to the Data Lake e.g. is it sensitive data, should it be anonymised before loading?
In addition, users of the data lake need to know who to contact if they have any questions about the data and what it can or cant be used for.
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Invest In Internal Training
Attaining good data quality is a difficult task. It requires a deep understanding of data quality principles, processes, and technologies. This knowledge is best obtained through formal training. Following the training track for a data management certification such as Certified Data Management Professional , Certified Information Management Professional , or Certified Data Steward would provide a good road map.
Encourage data quality staff to earn the certification, to better inform them on:
- Basic concepts, principles, and practices of quality management
- How quality management principles are applied to data
- How to think through both the benefits of high-quality data and the costs of poor quality
- How to create, deliver, and sell a business case for data quality
- The key principles in building data quality organizations
- Basic concepts, principles, and practices of a data stewardship program
- The data quality challenges that are inherent in data integration
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Option : The Access Control List Other Entry
The recommended approach is to use the ACL other entry set at the container or root. Specify defaults and access ACLs as shown in the following screen. This approach ensures that every part of the path from root to lowest level has run permissions.
This run permission propagates down any added child folders. The permission propagates to the depth where the intended access group needs permissions to read and run. This level is in the lowest part of the chain, as shown image below. This approach grants group access to read the data. The approach works similarly for write access.
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Keeping Data Lakes Relevant
Data lakes have to capture data from the Internet of Things , social media, customer channels, and external sources such as partners and data aggregators, in a single pool. There is a constant pressure to develop business value and organizational advantage from all these data collections.
Data swamps can negate the task of data lakes and can make it difficult to retrieve and use data.
Here are best practices to keeping the data lake efficient and relevant at all times.
How Databricks Addresses These Challenges
- Access control: Rich suite of access control all the way down to the storage layer. Databricks can take advantage of its cloud backbone by utilizing state-of-the-art AWS security services right in the platform. Federate your existing AWS data access roles with your identity provider via SSO to simplify managing your users and their secure access to the data lake.
- Cluster policies: Enable administrators to control access to compute resources.
- API first: Automate provisioning and permission management with the Databricks REST API.
- Audit logging: Robust audit logs on actions and operations taken across the workspace delivered to you.
- AWS cloud native: Databricks can leverage the power of AWS CloudTrail and CloudWatch to provide data access information across your deployment account and any others you configure. You can then use this information to power alerts that tip us off to potential wrongdoing.
The following sections illustrate how to use these Databricks features to implement a governance solution.
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Develop A Business Case
Ensuring buy-in and sponsorship from leaders is key when building a data governance practice. But buy-in alone wont fully support the effort and guarantee success. Instead, building a strong business case by identifying opportunities that data quality will bring may be helpful.
Improvements can include an increase in revenue, better customer experience, or efficiency. Leaders can be convinced that poor data quality and poor data management is a problem. But, data governance plans can fall flat if leadership isnt committed to driving change.
Lenovo Drives Revenue By 11% With A Cloud Data Lake
Lenovo, one of the worlds 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.
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.
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