EGT308 Data Engineering for Solution Architecture

Big data architecture is a framework for handling and managing massive datasets. It involves a series of steps to ingest, store, process, and analyze data effectively to generate insights.

A big data processing pipeline is a series of steps that transform raw data into actionable insights. It includes data ingestion, storage, processing, analysis, and visualization.

Data ingestion is the initial step in a big data pipeline. This step involves collecting data from various sources and preparing it for further processing.

Data storage refers to the techniques and systems used to persistently hold large datasets. This includes choosing appropriate storage technologies like databases, data lakes, or cloud storage services.

Data processing involves transforming and preparing data for analysis. This might include cleaning, transforming, and aggregating the data.

Data analytics is the process of extracting meaningful insights from processed data. It involves employing statistical methods and algorithms to discover patterns and trends.

Data visualization translates data into graphical representations like charts, dashboards, and maps. This makes it easier to understand and communicate complex insights.

Designing big data architecture involves selecting appropriate technologies and configuring them to optimize performance, cost, and scalability. It requires considering factors like latency, throughput, and data volume.

Visualizing answers using tools like Business Intelligence (BI) or feeding them into Machine Learning (ML) algorithms to make future predictions.

Having a system that allows users to easily: Find data assets, access metadata (like ownership and data classification), and ensure compliance with data governance rules.

The ability to trace data back to its origin, understand how it flows through the system, and visualize the data journey from source to consumption.

Making data accessible through secure authentication (credentials) and efficient network infrastructure.

Storing data in a format compatible with most internal processing systems, ensuring that data can be used across different systems.

Providing a clear schema (data structure) for data and attributing the data source to ensure data can be reused effectively.

Using a single tool to handle all stages of a data pipeline (from storage to visualization) creates a vulnerable system with potential breakdowns.

Using FLAIR data principles (Findability, Lineage, Accessibility, Interoperability, Reusability) helps to improve data architecture design and streamline data processes.

Choosing the right storage solution based on the characteristics of your data.

How organized and structured your data is, ranging from neatly formatted tables to unstructured text.

The speed at which new data becomes available for analysis.

The size of data being added to your system, impacting storage and processing needs.

Total volume of data stored and its growth rate over time.

The cost associated with storing and processing data in different locations.

Tools that help visualize data in meaningful ways, creating reports and dashboards.

A type of data that needs quick access and retrieval, often used in applications and web servers.

Data storage types, including traditional relational databases and NoSQL databases, designed for quick access and retrieval.

A database management system (DBMS) that uses tables with rows and columns to organize data in a structured format.

Data collected in files, often transferred from devices, and doesn't require immediate access.

A process of breaking down a big data pipeline into distinct stages like ingesting, storing, processing, and getting insights, for better efficiency.

The act of separating the process of data ingestion, storage, processing, and analysis into distinct stages, allowing for better control and scalability.

Tools and technologies that help you ingest data from various sources, process it, and store it for later analysis.

A specific type of big data architecture used for processing real-time data, often from connected devices like sensors.

A technique used in streaming data architectures to distribute data to multiple processing units, allowing for parallel processing and reduced latency.

Streaming data architectures rely on tools to ingest data continuously from sources like sensors, IoT devices, or log files.

In streaming data architectures, data is often stored in a way that preserves its order and allows for replaying events.

Streaming data requires real-time processing techniques, often using specialized technologies like stream processing engines.

Stream processing engines like Apache Flink or Apache Spark Streaming are designed to process data in real-time, with low latency and high throughput.

Streaming data analytics focus on identifying patterns, trends, and anomalies in real-time data, often enabling proactive decision-making.

Used to continuously analyze streaming data and generate alerts or trigger actions based on real-time patterns.

Apache Sqoop is a tool used to transfer data between Hadoop and relational databases, allowing data movement in both directions: importing data from relational databases into Hadoop Distributed File System (HDFS) and exporting data from HDFS back to relational databases.

Apache DistCp, short for distributed copy, is a tool used for efficiently copying large datasets within a Hadoop cluster or between clusters.

Apache Flume is an open-source software used to reliably collect, aggregate, and ingest large volumes of log data into Hadoop in a distributed manner. It's designed for handling real-time streaming data, making it ideal for tasks like log analysis and event processing.

A common pitfall in big data storage is using a single solution, often a traditional relational database management system (RDBMS), to meet all data storage requirements. This approach can be inefficient and costly. Instead, leveraging a combination of specialized storage solutions is recommended to optimize both cost and performance.

To streamline your big data storage strategy, consider using the right tool for the right job by carefully matching the specific characteristics of your data to the optimal storage solution. This approach ensures that your storage solution offers a perfect balance between cost efficiency and performance, promoting both responsiveness and budget-friendliness.

Stream data sources, like clickstream logs, are commonly ingested into platforms like Apache Kafka or Fluentd for real-time processing and analysis. This approach ensures that the data remains readily available for immediate insights.

Stream storage solutions, like Kafka, hold stream data to enable real-time processing and analysis. This immediate access to data empowers businesses to make quick, data-driven decisions based on the latest information.

Multiple open-source projects specialize in handling streaming data, including Apache Storm and Apache Samza. These tools enable reliable processing of large and unbounded data streams, allowing for real-time analysis and decision-making.