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Data Engineering and Best Practices
September 3rd, 2024 WRITTEN BY Debayan Ghosh, Manager, Data Management Tags: data management, Industry-agnostic
Written By Debayan Ghosh, Sr. Manager, Data Management
Data engineering is the backbone of any data-driven organization. It involves designing, constructing, and managing the infrastructure and systems needed to collect, store, process, and analyze large volumes of data and helps maintain the architecture that allows data to flow efficiently across systems. It serves as the foundation of the modern data ecosystem, enabling organizations to harness the power of data for insights, analytics, decision-making, and innovation.
At its core, data engineering is about transforming raw, often unstructured data into structured, accessible, and usable forms. This involves a wide range of tasks such as creating data pipelines, setting up data warehouses or lakes, ensuring data quality, and maintaining the integrity of data as it flows through various systems.
Why Is Data Engineering Important?
As organizations collect more data from various sources—such as customer interactions, business processes, IoT devices, and social media—the need to manage and process this data effectively becomes crucial. Without the infrastructure and expertise to handle large-scale data, companies risk drowning in information overload and failing to extract actionable insights.
Data engineering bridges the gap between raw data and meaningful insights by ensuring that data flows smoothly from various sources to users in a structured manner. It enables businesses to be data-driven, unlocking opportunities for innovation, optimization, and improved decision-making across industries.
In the age of big data and artificial intelligence, data engineering is a key enabler of the future of analytics, making it an indispensable part of the data ecosystem.
Role of Data Engineers in Data Engineering
Data engineers in this space are mainly responsible for:
- Data Pipeline Development: Creating automated pipelines that collect, process, and transform data from various sources (e.g., databases, APIs, logs, etc.).
- ETL (Extract, Transform, Load): Moving data from one system to another while ensuring that it’s correctly formatted and cleaned for analysis.
- Data Storage Management: Designing and optimizing databases, data lakes, and warehouses to store structured and unstructured data efficiently.
- Data Quality and Governance: Ensuring that data is accurate, reliable, and consistent by implementing validation, monitoring, and governance frameworks.
- Collaboration: Working closely with data scientists, analysts, and business teams to ensure the right data is available and properly managed for insights and reporting.
Best Practices in Data Engineering
Whether one is working on building data pipelines, setting up data lakes, or managing ETL (Extract, Transform, Load) processes, adhering to best practices is essential for scalability, reliability, and performance.
Here’s a breakdown of key best practices in data engineering:
- Design for Scalability
As data grows, so must the infrastructure. The design of data pipelines and architecture should anticipate future growth. Organizations should choose scalable storage solutions like cloud platforms (e.g., AWS S3, Google Cloud Storage, Azure Blob Storage) and databases (e.g., BigQuery, Redshift) that can handle an increasing volume of data. While working with large datasets that require parallel processing, we recommend considering distributed computing frameworks such as Apache Spark or Hadoop.
- Focus on Data Quality
Data quality is paramount. If the data is inaccurate, incomplete, or inconsistent, the insights derived from it will be flawed. Organizations must implement validation checks, monitoring, and automated alerts to ensure data accuracy.
Some key aspects of data quality include:
- Accuracy: Ensure data is correct and reflects real-world entities
- Consistency: Uniform data across different systems and time frames
- Completeness: Ensure no critical data is missing
- Timeliness: Timely availability of data
At Fresh Gravity, we have developed DOMaQ (Data Observability, Monitoring and Data Quality Engine), a solution which enables business users, data analysts, data engineers, and data architects to detect, predict, prevent, and resolve data issues in an automated fashion. It takes the load off the enterprise data team by ensuring that the data is constantly monitored, data anomalies are automatically detected, and future data issues are proactively predicted without any manual intervention. This comprehensive data observability, monitoring, and data quality tool is built to ensure optimum scalability and uses AI/ML algorithms extensively for accuracy and efficiency. DOMaQ proves to be a game-changer when used in conjunction with an enterprise’s data management projects such as MDM, Data Lake, and Data Warehouse Implementations.
To learn more about the tool, click here.
- Embrace Automation
Manual processes are often error-prone and inefficient, especially as systems grow in complexity. Automate your data pipelines, ETL processes, and deployments using tools like Apache Airflow, Prefect, or Luigi. Automation reduces human error, improves the reliability of the pipeline, and allows teams to focus on higher-level tasks like optimizing data processing and scaling infrastructure.
- Build Modular and Reusable Pipelines
Design your data pipelines with modularity in mind, breaking down complex workflows into smaller, reusable components. This makes it easier to test, maintain, and update specific parts of your pipeline without affecting the whole system. In addition, adopt a framework that facilitates code reusability to avoid redundant development efforts across similar processes.
Databricks as a unified, open analytics platform can be leveraged in building efficient data pipelines. Together, Databricks and Fresh Gravity form a dynamic partnership, empowering organizations to unlock the full potential of their data, navigate complexities, and stay ahead in today’s data-driven world.
To learn more about how Databricks and Fresh Gravity can help in this, click here.
- Implement Strong Security Measures
Data security is crucial, especially when dealing with sensitive or personally identifiable information (PII). Encrypt data both at rest and in transit. Ensure that data access is limited based on roles and privileges, adhering to the principle of least privilege (PoLP). Use centralized authentication and authorization mechanisms like OAuth, Kerberos, or IAM roles in cloud platforms.
In addition, ensure compliance with privacy regulations such as GDPR or CCPA by anonymizing or pseudonymizing PII and maintaining audit trails.
- Ensure Data Governance and Documentation
Data governance establishes the policies, procedures, and standards around data usage. It ensures that the data is managed consistently and ethically across the organization. Having proper documentation for your data pipelines, architecture, and processes ensures that your systems are understandable by both current and future team members.
Good practices include:
- Establishing data ownership and stewardship
- Maintaining a data catalog to document data lineage, definitions, and metadata
- Enforcing data governance policies through tooling, such as Alation, Collibra, or Apache Atlas
At Fresh Gravity, we have extensive experience in data governance and have helped clients of different sizes and at multiple stages in building efficient data governance frameworks.
To learn more about how Fresh Gravity can help in Data Governance, click here.
- Optimize Data Storage and Query Performance
Efficient storage and retrieval are key to building performant data systems. Consider the format in which data is stored—parquet, ORC, and Avro are popular columnar storage formats that optimize space and speed for big data. Partitioning, bucketing, and indexing data can further improve performance for queries.
Use caching mechanisms to speed up frequent queries and implement materialized views or pre-aggregations are appropriate to improve performance for complex queries.
- Adopt Version Control for Data and Pipelines
Version control, often associated with software development, is equally critical in data engineering. Implementing version control for your data pipelines and schemas allows for better tracking of changes, rollback capabilities, and collaboration. Tools like Git can manage pipeline code, while platforms such as DVC (Data Version Control) or Delta Lake (in Databricks) can help version control your data.
- Build Monitoring and Alerting Systems
Ensure that you’re continuously monitoring your data pipelines for failures, performance bottlenecks, and anomalies. Set up monitoring and alerting systems with tools like Prometheus, Grafana, Datadog, or CloudWatch to track pipeline health and notify data engineers of any issues. This can help detect and address problems before they escalate to larger issues like delayed reporting or failed analysis.
- Testing
Testing is critical in ensuring the reliability and correctness of your data systems. Implement unit tests for individual components of your data pipelines, integration tests to verify that the system as a whole works, and regression tests to ensure that new changes don’t introduce bugs. Test data quality, pipeline logic, and performance under different load conditions.
Some popular testing frameworks include PyTest for Python-based pipelines or DbUnit for database testing.
- Choose the Right Tools for the Job
There’s no one-size-fits-all solution for data engineering. Choose tools that align with your organization’s needs and goals. Whether it’s batch processing with Spark, stream processing with Apache Kafka, cloud services like AWS Glue or Google Dataflow, or a managed unified analytics platform like Databricks (that gives a collaborative environment with Apache Spark running in the background), select the stack that meets your use cases and data volumes effectively.
When evaluating new tools, consider factors like:
- Ease of integration with existing systems
- Cost-efficiency and scalability
- Community support and documentation
- Ecosystem and toolchain compatibility
How Fresh Gravity Can Help
At Fresh Gravity, we have deep and varied experience in the Data Engineering space. We help organizations navigate the data landscape by guiding them towards intelligent and impactful decisions that drive success across the enterprise. Our team of seasoned professionals is dedicated to empowering organizations through a comprehensive suite of services tailored to extract actionable insights from their data. By incorporating innovative techniques for data collection, robust analytics, and advanced visualization techniques, we ensure that decision-makers have access to accurate, timely, and relevant information.
To know more about our offerings, please write to us at info@freshgravity.com or you can directly reach out to me at debayan.ghosh@freshgravity.com.
Please follow us on LinkedIn at Fresh Gravity for more insightful blogs.
A Deep Dive into the Realm of AI, ML, and DL
January 3rd, 2024 WRITTEN BY Debayan Ghosh, Manager, Data Management Tags: artificial intelligence, deep learning, GenAI, Industry-agnostic, machine learning, neural networks
Written By Debayan Ghosh, Sr. Manager, Data Management
In today’s fast-paced world, where information travels at the speed of light and decisions are made in the blink of an eye, a silent revolution is taking place. Picture this: You’re navigating through the labyrinth of online shopping, and before you even type a single letter into the search bar, a collection of products appears, perfectly tailored to your taste. You’re on a video call with a friend, and suddenly, in real-time, your spoken words transform into written text on the screen with an eerie accuracy. Have you ever wondered how your favorite social media platform knows exactly what content will keep you scrolling for hours?
Welcome to the era of Artificial Intelligence (AI), where the invisible hand of technology is reshaping the way we live, work, and interact with the world around us. As we stand at the crossroads of innovation and discovery, the profound impact of AI is becoming increasingly undeniable.
In this blog, we embark on a journey to unravel the mysteries of Artificial Intelligence (AI), Machine Learning (ML), and DL (Deep Learning) where they not only keep pace with the present but, set the rhythm for the future.
Demystifying the trio – AI, ML, and DL
The terms Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are often intertwined.
At a very high level, DL is a subset of ML, which in turn is a subset of AI.
AI is any program that can sense, reason, act, and adapt. It is essentially a machine taking any form of intelligent behavior.
ML is a subset of that, which can replicate intelligent behavior, but the machine continues to learn as more data is exposed to it.
And then finally, DL is a subset of machine learning. Meaning, that it will also improve as it is exposed to more data, but now specifically to those algorithms which have multi-layered neural networks.
Deep Dive into ML
Machine Learning is the study and construction of programs that are not explicitly programmed by humans, but rather learn patterns as they’re exposed to more data over time.
For instance, if we’re trying to decide whether emails are spam or not, we will start with a dataset with a bunch of emails that are going to be labeled spam versus not spam. These emails will be preprocessed and fed through a Machine Learning algorithm that learns the patterns for spam versus not spam, and the more emails it goes through, the better the model will get. Once the machine algorithm is trained, we can then use the model to predict spam versus not spam.
Types of ML
In general, there are two types of Machine Learning: Supervised Learning and Unsupervised Learning.
For supervised learning, we will have a target column or labels, and, for unsupervised learning, we will not have a target column or labels.
The goal of supervised learning is to predict that label. An example of supervised learning is fraud detection. We can define our features to be transaction time, transaction amounts, transaction location, and category of purchase. After combining all these features, we should be able to predict the future for a given transaction time, transaction amount, and category of purchase, whether there’s unusual activity, and whether this transaction is fraudulent or not.
In unsupervised learning, the goal is to find an underlying structure of the dataset without any labels. An example would be customer segmentation for a marketing campaign. For this, we may have e-commerce data and we would want to separate the customers into groups to target them accordingly. In unsupervised learning, there’s no right or wrong answer.
Machine Learning Workflow
The machine learning workflow consists of:
- Problem statement
- Data collection
- Data exploration and preprocessing
- Modeling and fine-tuning
- Validation
- Decision Making and Deployment
So, our first step is the problem statement. What problem are we trying to solve? For example, we want to see different breeds of dogs. This can be done by image recognition.
The second step is data collection. What data do we need to solve the problem? For example, to classify different dog breeds, we would need not only a single picture of each breed but also, tons of pictures in different lighting, and different angles that are all correctly labeled.
The next step is data exploration and preprocessing. This is when we clean our data as much as possible so that our model can predict accurately. This includes a deep dive into our data, a look at the distribution counts, and heat maps of the densest points regarding our pixels, after which we reach the next step, modeling. This means building a model to solve our problem. We start with some basic baseline models that we’re going to validate. Did it solve the problem? We validate that by having a set of pictures that we haven’t trained our model on and see how well the model can classify those images, given the labels that we have.
Then comes decision-making and deployment. So, if we did a good job of getting a certain range of accuracy, we would move forward and put this in a higher environment (that includes Staging and Production) after communicating with the required stakeholders.
Deep Dive into Deep Learning (DL)
Defining features in an image, on the other hand, is a much more difficult task and has been a limitation of Traditional Machine Learning techniques. Deep Learning, however, has done a good job of addressing this.
So, suppose we want to determine if an image is a cat or a dog, what features should we use? For images, the data is taken as numerical data to reference the coloring of each pixel within our image. A pixel could then be used as a feature. However, even a small image will have 256 by 256 pixels, which comes out to be over 65,000 pixels. 65,000 pixels means 65,000 features which is a huge number of features to be working with.
Another issue is that using each pixel as an individual means losing the spatial relationship to the pixels around it. In other words, the information of a pixel makes sense relative to its surrounding pixels. For instance, you have different pixels that make up the nose, and different pixels that make up the eyes, separating that according to where they are on the face is quite a challenging task. This is where Deep Learning comes into the picture. Deep Learning techniques allow the features to learn on their own and combine the pixels to define these spatial relationships.
Deep Learning is Machine Learning that involves using very complicated models called deep neural networks. Deep Learning is cutting edge and is where most of the Machine Learning research is focused on at present. It has shown exceptional performance compared to other algorithms while dealing with large datasets.
However, it is important to note that with smaller datasets, standard Machine Learning algorithms often perform significantly better than Deep Learning algorithms. Also, if the data changes a lot over time and there isn’t a steady dataset, in that case, Machine Learning will probably do a better job in terms of performance over time.
Types of Libraries used for AI models:
We can use the following Python libraries:
- Numpy for numerical analysis
- Pandas for reading the data into Pandas DataFrames
- Matplotlib and Seaborn for visualization
- Scikit-Learn for machine learning
- TensorFlow and Keras for deep learning specifically
How is AI creating an impact for us today? Is this era of AI different?
The two spaces where we see drastic growth and innovation today are computer vision and natural language processing.
The sharp advancements in computer vision are impacting multiple areas. Some of the most notable advancements are in the automobile industry where cars can drive themselves. In healthcare, computer vision is now used to review different imaging modalities, such as X-rays and MRIs to diagnose illnesses. We’re fast approaching the point where machines are doing as well, if not better than the medical experts.
Similarly, natural language processing is booming with vast improvements in its ability to translate words into texts, determine sentiment, cluster new articles, write papers, and much more.
Factors that have contributed to the current state of Machine Learning are:
- Bigger data sets
- Faster computers
- Open-source packages
- Wide range of neural network architectures
We now have larger and more diverse datasets than ever before. With the Cloud infrastructure now in place to store copious amounts of data for much cheaper, getting access to powerful hardware for processing and storing data, we now have larger, finer datasets to learn underlying patterns across a multitude of fields. All this is leading to cutting-edge results in a variety of fields.
For instance, our phones can recognize our faces and our voices, they can look at pictures and identify pictures of us and our friends. We have stores where we can walk in and pick things up such as Amazon Go and not have to go to a checkout counter. We have our homes being powered by our voices telling smart machines to play music or switch the lights on/off.
All of this has been driven by the current era of artificial intelligence. AI is now used to aid in medical imaging. For drug discovery, a great example is Pfizer which is using IBM Watson to leverage machine learning to power its drug discovery and search for immuno-oncology drugs. Patient care is being driven by AI. AI research within the healthcare industry has helped advance sensory aids for the deaf, blind, and those who have lost limbs.
How Fresh Gravity can help?
Fresh Gravity has rich experience and expertise in Artificial Intelligence. Our AI offerings include Machine Learning, Deep Learning Solutions, Natural Language Processing (NLP) Services, Generative AI Solutions, and more. To learn more about how we can help elevate your data journey through AI, please write to us at info@freshgravity.com or you can directly reach out to me at debayan.Ghosh@freshgravity.com.
Please follow us at Fresh Gravity for more insightful blogs.