TOP 4 DATA INGESTION CHALLENGES
FOR BIG DATA-DRIVEN COMPANIES
Data ingestion is an essential step in the data analytics process, it involves importing data from external sources into a database or data warehouse for analysis and reporting.
Organizations would be unable to gain valuable insights and make informed decisions if they didn’t ingest data in an efficient and accurate way.
There are several factors to consider when choosing a data ingestion tool, including the volume and velocity of the data being ingested, the complexity of the data transformation and cleansing required, the security and compliance requirements of the data, and the integration with other systems and tools in the organization.
In this article, we will explore the challenges of data ingestion, and show you how to overcome them. We will also advise you on how to select the right tool for your organization based on your specific needs and requirements. This document serves as a comprehensive resource on data ingestion tools, covering the different types of tools available, their key features and capabilities, and the factors to consider when choosing the right tool for your organization.
Data Ingestion Challenges
Volume & Velocity
One of the most difficult aspects of data ingestion is dealing with the volume and velocity of the data being ingested. As organizations collect and generate more data from various sources, the amount of data that needs to be consumed can become overwhelming for data teams. This can lead to bottlenecks in the ingestion process and impact the overall performance and efficiency of the data analytics pipeline.
For example, in the service industry which has an extended customer list, companies might have issues to consolidating the huge data sets they’re extracting from CRM and ERP systems and other data sources into a unified and manageable big data architecture. IoT devices, such as smart sensors and connected devices, generate large amounts of data, often in real-time. This data is used to gain insights into the performance and behavior of the devices and the systems they are connected to.
However, the volume and velocity of data generated by IoT devices can be a significant obstacle. The data ingestion process needs to be able to handle the large amounts of data being generated and processed in real-time. This can be a complex and time-consuming process, and requires a detailed understanding of the data and the requirements of the processing and analysis systems.
To address this challenge, IoT companies often use stream processing and big data technologies to handle the volume and velocity of data generated by IoT devices. These technologies allow for the real-time processing and analysis of data, and can handle large amounts of data. Additionally, data management and data warehousing platforms can be used to store and manage the data generated by IoT devices.
Data transformation & Cleansing
Another challenge of data ingestion is data transformation and cleansing. In many cases, the data being ingested is raw and unstructured. It requires significant transformation and cleansing before it can be used for analysis. This can be a time-consuming and error-prone process and requires specialized skills and tools to handle efficiently. Data transformation and cleansing involves a variety of tasks, such as parsing, filtering, aggregating, and standardizing the data, and may require the use of specialized languages and frameworks, such as SQL and ETL (extract, transform, load).
This challenge of data transformation and cleansing can be seen in the healthcare industry. Electronic Health Record (EHR) are used to store and manage the patient information, such as medical history, lab results, and treatment plan. However, data from different EHR system might not compatible and have a different structure and format. When healthcare provider or researchers need to access to a patient data through different EHR systems, they often face the challenge of transformation and cleansing.
This process can be time-consuming and labor-intensive, as it requires a detailed understanding of the data and the requirements of the analysis systems. To address this challenge, healthcare providers and researchers often use data integration and data quality software to automate data transformation and cleansing. This can greatly improve the efficiency and accuracy of the data analysis process.
Data security & Compliance
Data security and compliance are also significant obstacles in data ingestion. Because organizations handle sensitive and personal data, it is critical to ensure that the data is secure and compliant with applicable regulations and policies. This can be a difficult task, especially when dealing with large amounts of data from multiple sources. Data security and compliance involve a range of measures, such as encryption, access control, and data masking, and may require the use of specialized tools and technologies, such as data loss prevention (DLP) and data governance.
In financial institutions, such as banks and investment firms, handles sensitive customer information such as personal identification numbers, bank account numbers, and financial transaction. This data is subject to strict regulations, such as the Payment Card Industry Data Security Standard (PCI DSS) and the General Data Protection Regulation (GDPR) to ensure the protection of customer’s personal information.
When ingesting data into a system, financial institutions need to ensure that the data is secure and compliant with regulations. This includes tasks such as encrypting sensitive data, implementing access controls, and monitoring for suspicious activity. This can be a significant obstacle as the data ingested into the system may come from various sources and in different formats, and ensuring compliance and security for all data can be a complex and time-consuming process.
To overcome this constraint, financial institutions often implement advanced security measures such as multi-factor authentication, intrusion detection and prevention systems, and security information and event management (SIEM) systems. Additionally, they use data governance frameworks and data quality software to ensure that data is accurate, complete, and complies with regulations.
Integration in systems
Finally, integrating the data ingestion process with other organizational systems and tools in the organization can be difficult. Because data ingestion is just only one component of the overall data analytics process, it is important to ensure that the data ingested can be easily accesible and usable by other tools and processes in the pipeline. This requires seamless integration and coordination between the different systems and tools involved. Additionally, this could include the use of APIs and integration frameworks, as well as the development of custom integrations and connectors.
For example, retail companies collect large amounts of data from various sources, such as point-of-sale (POS) systems, customer loyalty programs, and online sales platforms. This data is used to gain insights into customer behavior and improve business operations, such as inventory management and marketing strategies.
However, integrating this data into the organization’s systems and tools can be a significant obstacle. Data may be stored in different formats and structures, and may need to be integrated with existing systems such as enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, and business intelligence (BI) tools. This can be a complex and time-consuming process, and requires a detailed understanding of the data and the requirements of the organizational systems and tools.
Retail companies often use data integration and data management platforms to automate the data ingestion process and integrate the data with other systems and tools. These platforms allow for the integration of data from multiple sources, and can be configured to support different data formats and structures. Additionally, data governance frameworks can be used to ensure data quality and consistency across the organization’s systems and tools.
Shapelets is one such tool that offers a comprehensive solution for data processing and management. It standout features a storage engine that is optimized for time series data and can connect with various data stores, including common streaming data services. This makes it an ideal choice for organizations dealing with large amounts of time series data.
This versatility allows data teams to use Shapelets in a way that best fits their needs and resources. Additionally, Shapelets includes real-time monitoring, machine learning automation, and recommendations, as well as the ability to be deployed on a variety of platforms, including local computers, the cloud, and on-premise infrastructures.
In terms of processing capabilities, Shapelets incorporates highly efficient implementations of state-of-the-art algorithms for time series prediction, classification, and anomaly detection. It also allows users to use their preferred python libraries or even their own code implementations, which can be seamlessly deployed in computing clusters for maximum performance through distributed execution. This flexibility makes Shapelets a valuable tool for data professionals looking to use their preferred methods and techniques for data analysis.
For example, in our last performance study, we demonstrated the outstanding ingestion capabilities of a platform by using the New York Taxi dataset from 2009, which is 5.3 GB in size. To put this into perspective, a month’s worth of data is approximately 400 MB. We were able to complete the calculation in just 3 seconds, while other solutions may take up to 15 seconds. This shows a significant improvement in terms of time and resources.
Overall, Shapelets is a unique and valuable tool for businesses and data professionals looking to transform and empower their organizations through data-driven models. Its comprehensive approach to data management and processing makes it a powerful choice for data teams looking to efficiently manage and analyze large amounts of data from the early stages of collection and ingestion to storage and value extraction.
In conclusion, data ingestion is a crucial step in the data analytics process, as it involves importing data from external sources into a database or data warehouse for analysis and reporting. However, data ingestion also comes with its own set of challenges, including handling the volume and velocity of the data being ingested, managing the data transformation and cleansing process, ensuring data security and compliance, and integrating the data ingestion process with other systems and tools in the organization.
These challenges can be overwhelming for data teams, especially as the amount of data being generated and collected continues to grow. For this reason, it is important for companies to provide their data teams with effective tools to optimize their work and resources.