How do you decide which data to extract for data analytics?

1. Identify precise analytics goals through in-depth business process examination. 2. Use dynamic, predictive KPIs tailored to strategic objectives. 3. Assess all data sources for relevance and quality, covering structured and unstructured data. 4. Design schemas focused on analytics needs, ensuring compliance and ethics. 5. Choose between ETL/ELT and implement real-time streaming as needed. 6. Enforce metadata management and data quality standards. 7. Employ iterative prototyping for analytics models to refine strategies.

In my experience, it is best to know your business goals, data, data type, file type and volume beforehand. All this information can help you to decide better on process optimization and generating reports for data analytics. In my case - We had to spend a lot of time on data engineering to decide the final features, add extra inherited features or drop some existing features. The data volume is so high that we had to consider optimal file type (parquet in my case) and look for optimization w.r.t joins, partitions, number of nodes, executors etc. The data to select depends on the impact they create for specific goal.

Extracting any data depends on your use cases. Your data source may have multiple KPIs and metrics but if you have a very simple use for it then it doesn't make sense to extract all the KPIs. Also having a good knowledge of your data sources like how the data are stored and where you can get a particular data that you need helps a lot. So what I am trying to say is that it all boils down to your requirements. If you understand your requirements properly then knowing which data to extract becomes easy.

The extraction of data for analytics heavily relies on defining monitoring or tracking objectives. Identifying key performance indicators (KPIs) and determining the business decisions influenced by data analytics clarifies the selection of relevant data from diverse sources.

"Effective data extraction for analytics involves aligning with business objectives, prioritizing relevant and high-quality data, ensuring compliance, and collaborating with stakeholders. It's about extracting the right data to drive meaningful insights and decision-making.

Selecting data sources/formats hinges on relevance to goals, quality/reliability, accessibility, cost, and infra capacity, are involve: Profiling: Deeply analyze structure, content, and relationships. Cleaning: Tackle missing values, errors, and duplicates with sophisticated algorithms. Transform: Utilize techniques like machine learning for normalization, and complex encodings for analysis-readiness. Storage Solutions: Opt for hybrid storage (data lakes, warehouses) based on data characteristics. Security: Implement cutting-edge security and comply with stringent data regulations. Prioritize data that aligns tightly with objectives, ensuring it's of high quality, easily accessible, cost-effective, and manageable in volume/velocity.

Sometimes you can face vendor specific or legacy file formats. In this cases reading the documentation and working with other teams with similar experiences is really useful.

The choice of data sources and formats significantly impacts data extraction strategies. Your goals may require pulling data from diverse sources like databases, web pages, APIs, sensors, or social media, encountering structured, semi-structured, or unstructured formats. Assessing the quality, reliability, and accessibility of these sources and formats is crucial. Opt for data that aligns best with your extraction objectives, ensuring it's relevant and suitable for analysis. This careful selection process guarantees that the extracted data effectively supports your analytical needs, maximizing the value derived from your data analytics efforts.

Data sources and formats vary widely and can include databases (SQL, NoSQL), files (CSV, Excel, JSON, XML), APIs (REST, SOAP), streaming data (Kafka, Kinesis), and more. Each source/format has its own characteristics, advantages, and challenges, requiring different approaches for extraction, processing, and analysis. Honestly as a data engineer you might not get a chance to choose data sources and formats as it could have been defined long back, mostly in cases of data migration.

Data sources and formats vary widely. Sources can be internal, like databases or logs, or external, like social media or APIs. For example, Spotify uses its user activity logs and external music databases for recommendations. Formats can be structured (SQL, CSV), semi-structured (JSON, XML), or unstructured (text, images). Twitter, for instance, deals with a mix of text, images, and JSON data. Understanding these sources and formats is crucial for effective data extraction and processing.

After defining your data extraction goals, sources, and formats, selecting suitable extraction methods is crucial. Use SQL queries for relational databases or structured files to filter and manipulate data. ETL tools like Apache Airflow, Talend, or AWS Glue automate the extraction process, handling diverse data sources and formats efficiently. Web scraping tools such as BeautifulSoup, Scrapy, or Selenium are ideal for extracting data from web pages or APIs. For real-time data from sensors or social media, data ingestion frameworks like Apache Kafka, Spark, or Flume are effective. Choosing the correct extraction methods ensures an efficient and successful data extraction process, tailored to your specific analytical objectives.

Be clear on the data consumption needs, near real time or on-demand reports, think on the advantages of pull vs push. Also, consider when possible the creation of a data ingestion framework instead of a lot of individual data flows to simplify the management. In addition, evaluate advantages and disadvantages of ETL vs ELT. If you are planning to use the cloud, take a look on the pricing as well, you don't want a surprise later on.

Once your data extraction goal and sources are identified, selecting appropriate extraction methods is crucial. SQL queries filter, join, and manipulate data from databases or files. ETL tools automate extraction and handle diverse sources and formats. Web scraping extracts data from web pages or APIs, while ingestion frameworks handle streaming data. Tools like Apache Airflow, Talend, and BeautifulSoup streamline these processes. Implementing these methods ensures an efficient and effective data extraction process.

Data extraction methods depend on the data source and format. Web scraping is used for websites, like Google's web indexing. APIs are used for real-time data, like Twitter's tweet stream. SQL queries are used for relational databases, like Oracle's customer data. Text mining is used for unstructured text, like sentiment analysis on customer reviews. Image processing techniques are used for image data, like Facebook's facial recognition. Each method has its own strengths and challenges, and the choice depends on the specific data task.

A variedade de fontes e formatos de dados influencia diretamente as decis?es de extra??o de dados. é essencial avaliar a qualidade, confiabilidade e acessibilidade dessas fontes para garantir a relevancia e adequa??o aos objetivos de extra??o de dados.

Assuming the organization has a robust technical infrastructure for data extraction in place, I'd like to cover a couple non-technical challenges revolving around understanding requirements for the source system. 1. Understanding source schema & deltas, synchronizing schema modifications with the target platform & implementing mechanisms to capture, log, and apply data deltas are essential for maintaining data integrity. Your infrastructure is in place but you still need these configuration details. 2. Understanding the source systems normalization rules, e.g - if third normal form how to handle data storage & quality. If loose rules addressing through data cleansing or restructuring techniques to align source data with the target model.

Data observability tools are tremendously helpful when it comes to data extraction and analysis, which means data teams need to invest in tech debt and offload data governance and context knowledge in a centralized repository for all data consumer's benefits.

Data extraction faces multiple challenges, including issues related to data quality, security, privacy, scalability, and performance. Ensuring data accuracy necessitates thorough quality checks and validations. Adherence to data security and privacy regulations like GDPR or HIPAA is crucial to protect sensitive information. Additionally, optimizing extraction methods and tools is essential to manage growing and evolving data demands effectively. Addressing these challenges is key to securing reliable data for analytics, enabling informed decision-making and compliance with legal standards.

Um dos grandes desafios é manter a qualidade dos dados, principalmente em pipelines onde a disponibilidade ou a falta dela é crucial para o negócio. Geralmente esses pipelines vêm acompanhados de inputs manuais realizados pelo negócio, e o melhor caminho para garantir a integridade deste pipeline é construir um processo de Data Quality robusto que bloqueia arquivos com dados que comprometem a integridade e disparar alertas para o negócio realizar corre??es e refazer o input do arquivo.

The data integration process plays a crucial role in exploratory data analysis (EDA), especially in organizations that handle heterogeneous data sources. This process involves combining data from multiple sources to provide a unified view that allows for deeper and more meaningful analysis. However, the lack of homogeneity and data governance in many organizations poses significant challenges in terms of interoperability, which can greatly complicate EDA.

With the help of technical people it's easier to understand the possibilities and investment to achieve something. But with domain experts it's easier to steer stakeholders to know thier needs exactly. So to anwser "Which data to extract for data analytics?" you should talk to these two.

Focus on MVP. Instead of building reusable data products, ask your consumers their plan for actions and long term vision. Challenge them with the questions they're asking and provide alternative solutions. Some problems are meant to be simplified and some problems are meant to de overcomplicated, it all depends on the contextual knowledge of the data team and an inquisitive mind.

A extra??o de dados é crucial na engenharia de dados, mas traz consigo desafios como qualidade, seguran?a e escalabilidade. Garantir a confiabilidade dos dados e aderir aos regulamentos de privacidade s?o prioridades. A otimiza??o constante é necessária para lidar com as demandas em evolu??o. Essas considera??es informam as decis?es sobre quais dados extrair para análise.