How can you generate insights across different data platforms?

Try to build your own data lake with data principals as governance, observability , discovery as core. There are challenges of its own but having unified data store where any downstream can consume data irrespective of its technology or consumption plays important role in your go to market strategy.

Establish a comprehensive understanding of the data available across the platforms, including its structure, format, and quality. Utilize data integration tools and techniques to bring together disparate datasets into a unified data environment, such as a data lake or data warehouse. Implement data transformation and normalization processes to ensure consistency and compatibility across platforms. Use advanced analytics and ML algorithms to uncover patterns, trends, and correlations from integrated datasets, enhancing insights beyond individual analysis. Visualize the insights using interactive dashboards and reports to facilitate understanding and decision-making.

You can generate insights across data platforms through data consolidation into a unifying layer, such as a data warehouse, which brings together data from various sources. Connectors and ETL tools facilitate aggregating multi-source data into a single access point that enables running queries and analytics across the combined dataset. This allows you to gain a comprehensive view of business intelligence spanning different data platforms.

A data platform is a central hub that manages an organization's data, enabling its collection, storage, analysis, and use for various purposes. It acts as a foundation for data-driven decision-making and facilitates various data-related activities, including: * Data ingestion: Bringing data in from various sources. * Data storage: Housing the collected data in a secure and organized manner. * Data processing: Cleaning, transforming, and preparing the data for analysis. * Data analysis: Extracting insights and knowledge from the data. * Data visualization: Presenting data in a clear and understandable way.

Data platform is essentially an ecosystem to handle vast amounts of data. It encompasses various storage components like DWH, data lakes to facilitate analytical tools/machine learning engineers to extract the data in the easiest and fastest possible way. The data is usually modelled per one of the various data storing design patterns (e.g. lamdba, delta etc.) depending on various factors like velocity, volume, veracity of the data. These platforms plays a crucial role in moving an organisation to data centric decision making.

Data integration challenges are more common than you think and can be a significant hurdle in leveraging big data effectively. Here's why: - Different Formats - Quality Issues - Integration Costs - Compliance and Security Yet, overcoming these challenges is not just a necessity but a competitive advantage. Here's how: Start small: Focus on integrating data from a couple of sources first. Embrace technology: Use cloud-based platforms and data integration tools designed to handle diverse data formats and quality issues. Foster a data culture: Encourage your team to prioritize data quality at the source. Stay informed: Keep abreast of the latest in data privacy laws to ensure your integration efforts are compliant.

While ago there the business is running with limited number of IT applications and delay in reflecting the data from one application to another application is expected. Its not much needed to integrate the applications for quick reflection of the data across the applications in the organizations. But, Now a days data is growing fast and the systems expect the data reflection across the applications that are used in the organizations. So its much needed to have better software to perform data Integration. Some systems may need data processing while integrating. Could see there are better tools in cloud to perform these data integration.

Excel files, reports in .csv files, APIs, and databases from ERPs – we always encounter different types of data. Here are some thoughts and tips regarding these challenges: 1)Pause and try to comprehend the relationships among these different data sources. You should find something valuable from a business perspective. 2)Data Lakes have emerged to resolve this situation. Save your different data formats in separate paths; however, at this point, you can transform all data files into just one format, such as Apache Parquet. This way, all your data is now in the same format. 3)Pause and think about how you can join these different files to be able to answer your questions, such as "What was the most sold product last month?"

Most often we have to work with the data sources we are given, but... if you can, work with procurement, vendors and those selecting your operational systems, get your requirements on the table. The easier the pipelines are to build and manage the greater the ROI on the operational data. Have a plan that not only ingests data but takes it right through to your business layer and use cases. You don't need to deliver all of it at once but you'll gain buy-in and learn a lot from just one good use case.

As a data engineer, data integration is our daily battle! Here are some common challenges I face, with real-life examples: ? Data Silos: Information locked in different systems, like islands in an ocean. Imagine marketing data in Salesforce, finance in SAP, and customer service in Zendesk – a nightmare to connect! ? Language Barrier: CSV, JSON, XML – unifying formats is like translating a dozen books at once. ? Quality Chaos: Missing, inconsistent data is like building with faulty bricks – insights crumble. ? Performance Lag: Moving massive datasets can be slow and resource-intensive – think waiting hours for data to sync. But hey, that's why data engineers exist – to bridge the gaps and ensure your data sings in harmony!

Data platforms like Databricks - the platform I'm familiar with - solve these challenges: Unified data governance: Databricks' Lakehouse Federation capabilities in Unity Catalog allow organizations to build a scalable data mesh architecture with unified governance that enables efficient management of data across disparate platforms. Efficient querying and combining: Databricks enables users to efficiently query and combine data from multiple sources with a single engine, accelerating analysis and ensuring optimal query performance. Ensure data integrity: Databricks provides robust data governance capabilities, centralized auditing and integrated data reconciliation that ensure data integrity and compliance across multiple platforms.

A data platform is like a puzzle game; you need to understand what it should look like in the end, comprehend the pieces, and then start building it. You'll always need a service or script to make the ingestion (Python, FTP, Changed Data Capture etc); Something to schedule/orchestrate the entire process (cronJobs, Airflow, Mage, Prefect); A blob storage service as Data lake (AWS S3, GCP Cloud Storage ou Azure Blob Storage or MinIO); For data transform, you can use Python and SQL, or something more powerful, like DBT; For the last, a database to save and to query your data, like GCP Bigquery, Clikhouse or AWS Athena

To bridge these platforms, robust data engineering solutions are necessary. This involves using ETL (Extract, Transform, Load) tools like Talend, Apache NiFi, or cloud-based services like Azure Data Factory to extract data from various sources, transform it into a consistent format, and load it into a central data warehouse or data lake where it can be analyzed collectively.

It is always helpful to divide the solution implementation into smaller parts, where each part can be finished end to end in an iteration. This means always providing value with each part. A guaranteed way to failure is to try to deliver the entire solution at the end of the available time and budget, without ever releasing anything to production before. It is also convenient to separate the processing for each layer. Ingesting data from sources can be done in a number of ways, and if they hand over to the next layer in a standard form then that process can be loosely coupled. Always add all the required metadata to all layers so it is possible to understand the data, like technical and business sequence of events, change types/deletions.

While Integrating data from various sources perform the best data engineering practices. 1. Use the cloud based storage solutions as it has better back-up and data accessibility. 2. Build connections for the cross platform connectivity and interoperability, take security measures like use key pair authentication, OAuth or key vaults to store credentials. 3. Build pipelines to load data, do the data cleaning and apply needed transformations, use spark framework or Databricks, Snowflake platform, use optimised formats and load the data to downstream. 4. Build views for business to display the insights on any visualisation tool or portals like Salesforce etc. 5. Data modelling and designing is also an option if you are using On Prem systems.

As a data engineer, We're like a detective, using the right tools to analyze data & solve mysteries! Here are some popular techniques in my arsenal, with examples: ? Descriptive Stats: The who, what, & when. Summarize data with measures like mean, median, & standard deviation. ? Correlation Analysis: Finding hidden connections. See if variables are related, like website traffic & social media engagement. ? Regression Analysis: Predicting the future (sort of). ? Clustering: Grouping birds of a feather. Identify groups with similar characteristics. ? Time Series Analysis: Seeing the flow of time. ? Machine Learning: Teaching machines to learn. These are just a few of my trusty tools, & the best choice depends on the question at hand.

In Power BI, you can generate insights across different data platforms by leveraging its data integration capabilities. Utilize Power BI's connectors to seamlessly connect to various data sources like databases, cloud services, and files. Employ data modeling techniques such as relationships, calculated columns, and measures to create meaningful relationships and calculations. Leverage Power BI's visualization options to present insights effectively, allowing users to explore data dynamically. Finally, utilize Power BI's sharing and collaboration features to distribute insights across your organization.

Regardless of the planned analysis approach, always make sure the data in the integration layer is kept unchanged from any reporting or analysis requirements. Always prepare a separate serving/presentation/semantic layer/slice for each reporting/analysis use case (or group thereof), and clean/conform/align the data to that version of the truth only for that use case. When the next use case comes along with a different view of the world, it is trivial to provide another slice. This also allow for a fit for purpose serving model, using star schemas, snowflakes, obt's, api's or whatever the consumer prefers and the tools work best with

Power BI is designed to be user-friendly, with a relatively gentle learning curve, especially for those already familiar with Microsoft products. Its intuitive interface and drag-and-drop features enable users to quickly create reports and dashboards without extensive technical knowledge. It offers advanced data modeling features, such as DAX (Data Analysis Expressions), which is a collection of functions, operators, and constants that can be used in formulas or expressions to calculate and return one or more values. DAX is extremely powerful for creating complex calculations, custom measures, and aggregations.

Data engineers harness various data analysis techniques to unlock insights from integrated data. While tools like Tableau offer effective data visualization capabilities, data scientists leverage programming languages like Python and R to delve deeper into insights using advanced statistical methods such as causal inference and user life-time-value (LTV) modeling. Mastery of these complex statistical methods, especially among data scientists with PhD degrees, not only distinguishes them but also adds significant value to businesses. By employing these advanced techniques, organizations can make more informed decisions and gain a competitive edge in the market.

I have been dealing with data engineer since MAP reduce times and currently working with AWS and Snowflakes, here are the top skills if you want to unleash data engineering today(again it depends on project to project) 1) sql - this is what you will be doing 50% of the time, learn advanced analytical functions too here 2) python/pyspark - starting with Scala will limit you till just spark, hence my recommendation is to learn python as much as possible 3) be proficient in one cloud - AWS/Azure 4) Learn one BI tool - powerbi 5) start building pipelines - Airflow 6) once you are done with the above then you need to learn ML in the future (i can exp in the present) there will be a very thin line b/w engineers/ analysts/scientist AllTheBest

Don't forget data modelling and architecture. It doesn't have to be complicated but an organised data engineer is an effective data engineer!

A few key steps to understand of any dataset - Understand what various sources are delivering in terms of data (domain, assumptions etc.) - Try to understand the flow of data, how the current status updates, the facts and dimensions. - Read the data like it’s a story. That way, it’s easier to understand as well as to explain it. - Gather the relevant metrics of your data (if applicable) - Look into the sensitivity and integrity of the data before finalizing the solution. - Perform necessary operations and transformation to create the desired dataset. - Automate it into a pipeline using the most preferred tool (Azure Data Factory, Apache Airflow, etc.)

Understanding tool tradeoffs is key in data engineering. For example, for processing large-scale streaming data, Apache Beam might be preferable to Spark Streaming due to its unified batch and streaming processing capabilities. Conversely, for small stream ingestion, a Kafka-based solution could be seen as overkill, introducing unnecessary complexity. Assessing factors like scalability, performance, and maintenance overhead helps in choosing the right tool for optimal data processing pipelines.

Always make sure the business model comes first. Technical skills and tools tend to miss the mark without clear, business knowledge and non-technical guidance. Always gather all the metadata into your metadata model. Use this to both validate the business model assumptions of the data and to model the physical implementation. Use it to automate repetitive/pattern-based tasks, to track lineage, to be the foundation for (automated) documentation, for spec checking, to build graphical representations of relationships, flows, to support naming conventions, data type consolidation and more.

Just make the data trustworthy before using it. It helps if the data is in one common location but nowadays insights tools can connect to various sources and help generate reports!

Never judge the data, or try to make it right coming in to the environments. The source data is what it is. If there is a need to clean, or conform the data, then that must always come later. Provide a way for stakeholders and data stewards to manage the data so that both the original version and the changes are available. Provide error reports, push back any required maintenance to the source. It should never be the Integration team's responsibility to conform the data into the version of the truth required by the end product, only to provide the means to do so by the owners/stewards of the data/sources.

From my point of view, it’s important to understand the purpose for what you need the data. The big picture of the business requirements defines the data platform strategy including all the features needed to develop data products as well as aspects of data protection and data security. To begin with, it’s sometimes easier to start with a smaller data use case / data product and shape clearly the business value, and then build incrementally further cases.

In Recent times, when we are talking about data. it is much needed to talk about data privacy. When Data is growing it is becoming difficult to secure the data. It would be better the privacy also considered when we talk about data platforms

I believe that even with the use of data platforms as the literature suggests, the great takeaway we have with the use of this technology is to be able to unify the data sources through the platform. Some options for solving these challenges is the use of data products so that we can have a clear view of the exit and entry data contracts, and this allows the teams' needs to be met by the data, wherever they are, just with the view of which data are necessary. The product approach allows us to have a clear view of the data discovery by the teams that need their uses.