What are the best practices for data ingestion?

For adequate data ingestion, pick a batch, real-time, or event-driven method that fits your objectives and is compatible with your data sources. Prioritize data validation and cleansing to enhance data quality. Use data compression, partitioning, and parallel processing to optimize performance. Secure your data with encryption and access controls to protect against unauthorized access. Employ automation tools like Apache Airflow or AWS Glue to reduce errors and streamline the process. Regular reviews are vital in maintaining a reliable, efficient data ingestion process.

In my current role at TransUnion, selecting the appropriate data ingestion method is crucial. I often utilize batch ingestion for large, stable datasets from banks, which don’t necessitate immediate analysis, processing them at scheduled intervals. Conversely, for dynamic, time-sensitive data requiring swift insights, such as consumer behavior trends, I employ streaming ingestion. This real-time approach is vital for providing immediate, invaluable insights, allowing our clients to promptly optimize their strategies. Balancing between these methods is essential for maintaining efficiency and delivering timely, impactful insights.

While organisations might need both Batch and Streaming ingestion based on the kind of sources they have, it's also important to move in the direction of self service and reduce manual intervention as much as possible.

For an effective data ingestion process, consider starting with these key questions: - Where is the data located? (Databases, APIs, object stores like S3, etc.) - Which specific data is required? (We don't always need everything.) - Why do we need it? (The use case) - What data quality checks are essential? - How large is the data? (Helps determine if simple Python or distributed programming like PySpark is best.) - What's the suited ingestion method (batch or real-time) ? - How should we be alerted about the ingestion status eg. Slack notifications ? (To identify failures quickly) - What work could be automated? A deep understanding of how data will be used aids in choosing the right data formats, schedules, and quality checks.

If the end data consumer does not need the updated data in real time, we should consider batch processing. We can opt for an increased frequency for more frequent and up-to-date data. Realtime ingestion becomes necessary where the decision-making is also real-time like ride-hailing, log analytics, fraud detection etc.

In my role at TransUnion, maintaining impeccable data quality is crucial. I've instituted rigorous validation and cleansing protocols during data ingestion to ensure accuracy and reliability. By employing advanced frameworks and algorithms, discrepancies are swiftly identified and rectified, maintaining the integrity of our datasets. This meticulous approach is pivotal in delivering precise and reliable insights to our clients, ensuring the efficacy of our analytical models. Balancing precision with efficiency is key in managing extensive datasets and sustaining high standards of data integrity.

Though data-cleansing is a crucial step, most cases it is better to keep the raw data as it is, and then apply any cleansing/transformation, it helps to start-back from the initial process again, if there is some errand.

In every organization data is key. To understand the data or make it meaningful, data validation or data cleansing is very crucial. Some of the techniques to validate and cleanse your data: 1. Enforce referential integrity, primary key constraints, and foreign key relationships. 2. Validate data against specific business rules 3. Identify and eliminate duplicate records to maintain data accuracy. 4. Standardize data formats (e.g., dates, currency) for consistency. 5. Data validation and cleansing should be an ongoing process, especially in cases where new data is constantly ingested.

Even the slightest difference can create a significant improvement while ingesting big data. For example, using Parquet files instead of CSV files can significantly improve the i/o operations and querying. Parquet files are smaller than CSV files and can be read and written much faster than CSV files.

In my role at TransUnion, enhancing data ingestion performance was pivotal. I led efforts to establish robust network connections and minimize data volume through strategic filtering and aggregation before ingestion. By adopting uniform data formats and utilizing advanced compression techniques, we managed to cut down on data size and transfer times significantly. The introduction of parallelization techniques further elevated our ability to handle large datasets efficiently, optimizing our data ingestion processes and strengthening our analytical prowess.

Based on the selected tools, insights into enhancing performance become evident. For instance, certain cloud-based tools demonstrate superior performance when handling columnar data formats such as ORC and PARQUET. Tools like Apache Spark show heightened efficiency with increased Data Processing Units (DPUs). Additionally, utilizing compression can expedite data transfers which helps with storage space, and reduces disk read times.

In my experience, it is important to understand your ingestion system and the type of file you process. An optimized ingestion for a batch load is the system where the tools or scripts can perform parallel executions of the files. With parallel execution, the system should not be overburdened with the file size. In an ideal scenario, the file size should be in the range of 200-300 MBs. If the file size is large, then it should be divided into chunks to process them better. Secondly, the type of file that you process plays a vital role in optimizing the workload. Formats such as parquet, avro, or orc are designed for big data applications.

Optimize data formats: Choose efficient data formats that minimize storage and processing overhead. Formats like Parquet, ORC, or Avro are commonly used for efficient data storage and ingestion. Compressing the data can also reduce the amount of data transferred and improve ingestion performance. Use parallel processing: If possible, parallelize the ingestion process to handle multiple data sources or partitions simultaneously. This can significantly improve ingestion throughput. Consider using distributed processing frameworks like Apache Spark or Apache Flink to leverage parallelism. Implement data partitioning: Partition your data based on relevant attributes to improve ingestion performance.

At TransUnion, I led efforts to fortify data security during ingestion, implementing advanced authentication protocols and encryption algorithms like AES to safeguard data. These measures, essential in both data storage and transit phases, ensured the integrity and confidentiality of sensitive information, reinforcing the trust of our clients and partners in our data handling practices.

Database auditing is a very important feature available in many Databases with which activities in multiple users environment can be tracked and audited. It provide different granular level such as users, roles, groups for which all or subset of activities can be audited. When it comes to authentication, apart from many other things it becomes important which authentication method to use and also how the passwords are stored and fetched. It is also important to have necessary monitoring in place for accounts which are static and dynamic.

Data security and data encryption are very important to protect sensitive information and maintain data integrity. Some of the best practices for securing and encrypting data during the ingestion process: 1. Secure Socket Layer (SSL) or Transport Layer Security (TLS) for ensuring data is encrypted during transit. 2. RBAC to control user access based on roles and responsibilities, limiting access to sensitive data. 3. Data At-Rest encryption when it's stored in databases, file systems, or any storage medium. 4. Mask sensitive data in non-production environments 5. Implement logging and monitoring to track data access, changes, and user activities. 6. Regular Audits

At TransUnion, I prioritized creating detailed documentation for every facet of our data and implemented robust monitoring solutions to oversee our data ingestion processes. This approach ensured transparency and allowed for immediate identification and resolution of any issues, optimizing the overall efficiency and reliability of our data systems.

In some of my projects due to lack of documentation I faced many challenges, so documentation (TDD, FDD etc. ) is very important in every process. Documentation helps organizations/teams to understand the design & architecture, process and even sometimes helps in troubleshooting. Key things: 1. Documentation should clearly explain the entire data ingestion workflow, including systems, tools, and technologies used, data sources, ingestion methods, transformations, and storage. 2. Keep the documentation updated with any modifications or enhancements made to the process. 3. Document error handling procedures and exceptions that help the team to troubleshoot the issues. 4. Monitor processing times, and usage to ensure optimal performance.

Define data quality metrics: Determine the key metrics that you want to monitor to ensure data quality. This could include metrics like data completeness, accuracy, timeliness, and consistency. Clearly define the thresholds or benchmarks for each metric to establish acceptable levels of data quality. Implement data ingestion monitoring: Set up monitoring tools or systems to track the data ingestion process in real-time. This can include monitoring the status of data sources, data transformation steps, and data loading into your target system. Use alerts or notifications to flag any issues or anomalies that may arise during the process.

Create a logical/physical layer that distinguishes between raw, 'untouched' data and transformed data that passes cetain quality checks and is fit for downstream consumption.

Create a data quality framework that can be pluggable to any type of data ingestion or processing pipeline, Also having a store to manage data quality rules and rule output is important. This data can be used to track the data quality metrics over time.

One thing that I've found helpful is to embed telemetry and observability into your ingestion framework for better tracking of the defects that may occur in a complex framework. It helps in making the entire ingestion framework a robust model for further replicating it across different Lines of business.

Study the Source System Define your Entities Categorise your entities for use case Prepare Meta Data config Taa-Daa ????♂???