Data pipeline allows to connect one or more data sources together. It is often used in a data warehouse for ingesting data from siloed data sources to achieve a unified view of data.
What’s data pipeline?
Creating data pipelines involves many steps, that can be simplified to 3stages: Extract - pulling data from the data source, Load - transferring datato an intermediary storage and loading data to target datastore. To simplifythis process data engineers often use specialized tools that help managingthese steps. Two prime examples of such tools are Airflow and Luigi that werecreated by companies Airbnb and Spotify and made open source.
ETL vs ELT
Traditionally data pipelines tend to follow a predefined process (ETL) comprised extracting, transforming and loading data into a data warehouse. One of the serious drawbacks of this process is the lack of flexibility: the analysts have to predict every data use before a report is created. Simple access to the information is not available until the whole process is completed, and every change is costly and risky. ETL approach was once popular because of the costly on-premises computing and data storage.
With the growing popularity of cloud data warehousing and the plummeting cost of cloud data products, ETL approach is becoming less and less popular. In order to switch to agile data management, many enterprises change from ETL to ELT (extracting, loading, transforming), where data transformations are made after the data is loaded into a data warehouse.
The analysts do not have to predict the insights they want to generate because they can load data before transforming it. Overall, ELT offers many benefits for the enterprise: it insures more flexibility, data literacy and cost-efficiency.
How can data become agile?
Very often development of data warehouse is done by traditional waterfall approach: analysis, design, testing and then implementation. Agile data warehousing is a modern value-driven approach in software development and data management that implies close cooperation between all the stakeholders: developers, business experts, project managers, sponsors and others, who have a goal to make better data-driven decisions.
Modern approach in Data Warehousing
The conventional approaches of data warehousing cannot handle the explosion of data we are facing now. The modern data warehouses are primarily built for data analysis. They focus on business value rather than transactional records. The modern approach in data warehousing enables the real time data visualization, which allows making better decisions for the business.
Cloud data products
Recently we saw the rise of cloud data warehouses. Companies move their workloads into the cloud, because of its ease of use, potential to analyze bigger sets of data, save large amounts of money, and avoid the difficulties of managing on-premises clusters.
Such cloud data products as Redshift, BigQuery, Snowflake, Azure SQL Data Warehouse are fully-managed data warehouse services in the cloud, that enable you to provision your cluster, upload your data set and then perform data analysis. Thanks to its instant elasticity, cloud data products allow you to optimize your workloads in minutes. With the powerful streaming ingestion capabilities, these products allow the companies to analyze data in real time and have up to date insights.
The ongoing price war between storage service providers caused the plummeting of storage costs. It makes the cloud data warehouses more cost efficient. Another reason of wide use of cloud data products is the explosion of data. Given the exponential growth of data, cloud data warehouses make it easy to scale the infrastructure seamlessly.
The open source revolution changed the way of data storage. Companies that have traditionally relied on commercial databases, are changing to open source cloud products. Open source products enable companies to move quicker to market, don't require paying licensing fees and allow to collaborate with other companies in improving the product for everyone. One of the primary examples of this kind of product is Hadoop that allows companies to create big data sets without any limitation to storage size.
Of course open source is not a silver bullet and has some of the drawbacks, such as: solutions are more brittle (can break easily), requires specialized expertise that is expensive and hard to find; lack of support can make it difficult to add new features that might be required for the business.
Data Lake and Streaming Real Time Data
Data lake is defined as centralized repository to store all structured and unstructured data at any scope. Compared to data warehouse, the structure of data or schema is not defined when data is captured. It is mostly designed for quickly changing data.
Ingesting data is possible in two ways - batch and stream. While ETLcontinues to be a good choice for legacy data warehouses, it is limited tobatch data load. On the other hand, ELT not just supports both batch and streamloading but it also enables companies to get value from the data faster,resulting in a more agile data warehouse.
If your company works with the data that comes continuously and quickly, it means you might have streaming data that requires different approach. With the help of powerful open source tools (such as Kafka, Storm, Flink), you can easily process real-life data streams and immediately react to ever-changing conditions in real time. This provides your business with the ability to react in minutes rather than days when core business indicators change.
Cloud data warehouse benefits
Data warehousing brings a wide range of advantages for your business:
· Understanding key business drivers. Data warehousing helps understand better what customer data drives your business. Thanks to combining data from multiple sources, it enhances your business intelligence by giving you a complete view of your enterprise.
· Optimization of customer experience and personalization. Designed to support the customer-centric analysis of your business, data warehouse helps you understand better the behavior and needs of your customers, improve their experience and create more personalized approach, bring more loyalty and increase customer retention.
· Competitive advantage in making data driven decisions. The actionable insights you get from collected and analyzed data help you make data driven decisions and, as a result, bring returns on investment and make your business competitive. The competitive advantage includes tracking annual recurring revenue, preventing customer churn, increasing product usage, optimizing operations and modifying marketing campaigns.
· Unification as customer data as CDP. Already having a data warehouse in place makes the implementation of CDP (Customer Data Platform) easier and, therefore, cheaper. It gives you a 360-degree view of your customers and helps you integrate all the data sources together into a single repository.
Main challenges of cloud data warehouse
Despite numerous benefits of data warehousing, there some challenges thatyou might encounter.
· Siloed data. Modern big data is scattered across many sources, for example, ecommerce might need advertisement data, SEO data, clickstream data and many other sources to be able to see the full picture of their online business. This creates a problem of bringing this data together, which involves creating a unified schema, cleaning up source data and making sure it’s easily accessible to the business stakeholders.
· Integration overhead. Bridging data together requires developing data pipelines. This requires significant effort due to complexity of different data sources. Even after building integrations they tend to break easily which requires constant engineering support to fix them.
· Maintenance. Operational cost of data warehousing keeps rising together with data volumes, which makes it harder for companies to justify managing it on-premise, due to infrastructure and engineering costs.
· Scarcity of talent. The shortage of talent in the field is significant. According to Business.com, 40% of companies struggle with finding and retaining big data specialists. The demand for these professionals is greatly exceeding the supply, which brings more challenges to modern data warehousing.
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