What are the most effective data warehousing practices during acquisition?

Identifying data sources and requirements involves pinpointing where your data is coming from and understanding what information is needed to meet your business objectives. This initial step ensures that you gather the right data from the right sources, setting a strong foundation for your data warehousing efforts.

this includes using distributed storage platforms like Hadoop file systems or cloud storage, offering expandable capacity as needed. Implementing compression and partitioning techniques can optimize storage space usage and enhance query performance. Additionally, defining security policies and access controls is crucial to safeguard data during storage and transmission, ensuring compliance with privacy and security regulations. Utilizing real-time data monitoring and management systems helps identify and address storage issues promptly, ensuring data integrity and availability

First, Know Your Data Sources: Internal or external, structured/unstructured, batch/streaming - understand their characteristics, formats, and locations. Second, Volume, Velocity, & Quality: Frequency, data amount, and accuracy are crucial for planning. Finally Define Your Needs: What data do you need? Why? Who will use it? This clarity guides acquisition strategies and tool selection.

It all boils down to your data: Different techniques excel with different data types and goals. Structured & Batch Data: ETL (Extract, Transform, Load) shines here. It extracts data, cleans and transforms it outside the warehouse, then loads it for analysis. Unstructured & Streaming Data: Need flexibility? Consider ELT (Extract, Load, Transform). Load the data first, then transform it within the warehouse, ideal for evolving data structures. Incremental Updates: For continuous data streams, leverage Change Data Capture (CDC). It captures only the changes since the last update, minimizing processing time. Real-time Access: Data Virtualization creates a virtual view of your data, offering real-time access without physically moving it.

Choosing etl/elt plays really significant role based on the pipeline and usecases. For example dumping data from asymetrical format to data warehouse format required etl which is extract from the data source then transform data through pipeline with let it be python code or any etl tool like informatica and load into data warehouse. Where if you are let's say using tableau so it will use elt where you are extracting data then loading data into the dashboard's memory and in tableau transforming data to visualize it.

Choosing the right data integration techniques involves selecting methods that efficiently collect and consolidate data from various sources into the data warehouse. This includes options like Extract, Transform, Load (ETL), Extract, Load, Transform (ELT), or real-time data integration, depending on factors such as data volume, velocity, and complexity.

Always checking the quality of data beforehand or giving requirements to the team before you to get the data in xyz format helps a lot in building pipelines a lot more smoothly and faster. So either that way or through scripts always try to get the good quality data beforehand itself which should include validation checks in place so a couple of rows in millions of rows don't fail your etl or elt pipeline!

Implementing data checks and validation during the acquisition phase of data warehousing involves verifying the accuracy, completeness, and consistency of the data being acquired. This process helps ensure that the data being brought into the warehouse is reliable and suitable for analysis, minimizing errors and enhancing the quality of insights derived from the data.

Think of it like inspecting your building materials. Here are some techniques: Data Profiling: Get to know your data! Analyze its structure, identify missing values, and check for inconsistencies. Data Cleansing: Clean up any errors you find. This could involve fixing typos, filling in missing values, or removing duplicates. Data Standardization: Ensure consistency across your data. For example, standardize date formats or measurement units. Data Enrichment: Add additional data points to enhance your analysis. Think of it as adding insulation to your house for better results! Data Auditing: Track changes and ensure data integrity throughout the process. This is like having a quality control inspector on your data construction site.

here's what you need to know: Fortress around your data: Implement robust security measures like encryption, authentication, and authorization to safeguard your data from unauthorized access or modification. Regularly back up your data and establish a recovery plan for any potential mishaps. Compliance is key: Adhere to data privacy regulations like GDPR and CCPA to ensure ethical data handling. This builds trust and protects your reputation. Security from source to destination: Every step of data acquisition, from its origin to its final resting place in your warehouse, needs to be secured. Choose the right tools and methods to create an impenetrable shield. By prioritizing data security, you prevent breaches, leaks, and misuse.

Most of the time whenever you are performing etl or elt data, the column's entire row data will not be useful. Let's say phone number or social security number could be a potential risk if it gets exposed even internally to extra or dev teams as prod testing data so always secure and protect your data.

During data acquisition, securing and protecting your data involves implementing robust encryption methods, access controls, and regular security audits to safeguard against unauthorized access or breaches.

Most of the times when the data acquisition phase takes place, it consumes most of the memory and ram power to run python/shell scripts in terms of manipulating it and may be databases, local systems/servers or cloud to store the file or any formatted data. And each aspect costs a lot to the company or org so it does needs to be monitored and optimized by using encryption methods whenever required to reduce the cost and enhance the performance!

"Monitor and optimize your data acquisition performance" involves regularly tracking the efficiency and effectiveness of your data acquisition processes, identifying any bottlenecks or inefficiencies, and implementing measures to improve them. This practice ensures that data is acquired in a timely manner and at the desired quality level, facilitating smooth data warehousing operations.

Documenting and communicating your data acquisition process involves outlining the steps taken to gather, store, and manage data. It ensures transparency and understanding among stakeholders, promoting trust and alignment within the organization.

The primal best practice on case of to acquire data will store in data warehouse is transform & integrate it in your operational data store (ODS) first and send a copy this record in your data warehouse to handle the temporality and persistency aspect.