In this article, we will briefly talk about Data Engineering and its pipelines. Data engineers plan and construct pipelines that transport and transform data into a specific format where it comes to the Data scientists or other users, by the time, it is in an extremely operational state. These pipelines need to take data from a lot of distinct sources and gather them into a sole warehouse that signifies the data regularly as a single source of truth.
Sounds pretty simple enough but numerous skill goes into this part. This is why there is a shortage of Data Engineers and why there is confusion around the part. The following figure is one example of the actions and activities elaborate in data engineering.
What is Data Engineering?
Data engineering is a collection of processes and operations intended to build interfaces and techniques for the access and flow of information. It needs enthusiastic specialists, called data engineers, to maintain and keep up data so that it remains accessible and functional by others. In short, data engineers arrange and operate the data infrastructure of the organization making it for more analysis by scientists and data analysts.
Explaining Data Engineering and Data Warehouse
To recognize data engineering in simple words, let’s go to databases that are collections of accessible and consistent information. Within an enterprise business, usually, there are various diverse types of software of processes management such as CRM, ERP, production systems, etc. Also, there are a lot of diverse databases as well. As there are multiple data sources, having data stored all over in different formats stops the business from exploring the clear and full picture state of their business. It’s compulsory to discover how to acquire sales data from its devoted database to access with inventory records saved in a SQL server. This makes the requisite for integrating data in a combined storage system where data is gathered, reformatted, and ready for use stated as data warehouse. Now, business intelligence (BI) engineers and data scientists can attach to the warehouse, get access the required data in the desired format, and start getting valuable insights from it.
The procedure of transfer data from one system to another is maintained by data engineers. However, the responsibility of a data architect is to build a Data Warehouse, planning its structure, defining the sources of data, and selecting an integrated data format.
Here is going the process of the data flow in detail, explain the hints of creating a data warehouse, and define the role of a data engineer.
A central repository where raw data is converted and put in storage in query-able format. Without Data Warehouse, data scientists have to fetch data directly from the production database and may fetch up reporting diverse outputs to the same question. Attending as an enterprise’s sole source of fact, the data warehouse makes simpler the organization’s analysis and reporting, metrics forecasting, and decision making.
Logically, a data warehouse is just a relational database heightened for analysis, collecting, and querying large volumes of data. Amazingly, Data Warehouse is not just like a regular database.
- Firstly, they vary in terms of data structure. A normal database normalizes data without any data redundancies and splits related data into tables. This consumes many computing resources, as one query gathers data from many tables. On the other hand, a Data Warehouse uses simple queries along with a small number of tables to improve analytics and performance.
- Secondly, intended at day-to-day transactions, usually, a database does not store historic data, while in terms of warehouses, it is the main purpose of them, as they gather data from many periods. Data Warehouse makes easy a data analyst’s job, permitting for manipulating all data from a single interface and originating analytics, statistics, and visualizations.
- Remarkably, a data warehouse does not support as many users, operating at the same time, as a database.
To build a data warehouse, four simple components are joined.
1. Data warehouse storage.
The base of data warehouse structural design is a database that stores all enterprise data permitting business employers to access it for fetching valuable insights. Usually, Data architects choose between on-premises and cloud-hosted databases observing how the business can help from that solution. As cloud environment is more low cost, is not restricted to a prescribed structure, and easier to scale up or down, it might fail to on-prem solution in terms of querying security and speed.
In addition to business context to data, metadata assists make over it into comprehensible knowledge. Metadata describes how data can be altered and processed. While loading data into the data warehouse, it holds information about any transformations and processes applied to source data.
3. Data warehouse access tools.
Data warehouse access tools intended to facilitate dealings with Data Warehouse databases for business employers, access tools require to be integrated with the warehouse. Another type of access tool is known as data mining tools. Data mining tools automate the process of discovering patterns and relationships in large quantities of data centered on advanced statistical modeling methods.
4. Data warehouse management tools.
Traversing the enterprise, data warehouse treat a number of administrative and management operations. That’s why managing a Data Warehouse needs a solution that can help all these processes. Devoted data warehouse management tools are to achieve this.
Building Data Warehouse: Understanding the Data Pipeline
While data warehouse deals with the storage of data, data pipelines make sure the utilization and handling of it.
A Data pipeline is a sum of processes and tools to perform data integration.
The primary responsibility of data engineering is Building data pipelines. It needs advanced programming skills to build a program for automated and continuous data exchange. As this process is fairly difficult, it is feasible for businesses whose products have set up the market, to hunt more progress. A data pipeline is generally used for:
- transferring data to the data warehouse or to the cloud
- wrangling the data into a sole location for convenience in data science and machine learning projects
- integrating data from several linked devices and systems in IoT
- fetching data to one place in Business Intelligence for well-versed business decisions
Creating a data pipeline step by step
Pipeline infrastructure be different according to the scale and use case. However, a set of ETL operations always implemented:
1. Extracting or pulling data from source databases
2. Transmuting data to match a unique format for particular business commitments
3. Loading data that is reformatted to the data warehouse
1. Retrieving incoming data.
At the beginning of the pipeline, we have to face raw data from many distinct sources. The responsibility of a Data engineer is to write pieces of code that run on a program extracting all the data collected during a specific period.
2. Standardizing data.
Often Data from distinct sources is vary and inconsistent. So, for efficient and well organized analysis and querying, it must be modified. Having data put in, engineers run another set of codes that make change it to meet the requirements of format (e.g. dates, units of measure, and attributes like size or color.) Data transformation is a sensitive job, as it considerably improves data usability and discoverability.
3. Saving data to a new destination.
After getting data into an operational state, data engineers can load it to the end point that normally is a relational database management system (RDBMS), Hadoop, or a data warehouse. Each end point has its particular practices to follow for reliability and performance.
4. Maintaining alterations.
Even though being automated, a data pipeline must be continuously maintained by data engineers: they restore it from failures, update the system by adding or deleting fields and tables, or modify the schema to the changing requirements of the business.
Data pipeline challenges
Setting up safe and consistent data flow is a puzzling job. There are so numerous effects that can go incorrect during data transportation: Data can be dishonored, hit restricted access bring about latency, or data sources may clash making duplicate or incorrect data. Receiving data into one place needs cautious designing and testing to sort out junk data, removing duplicates and mismatched data types, to obscure sensitive information while not missing sensitive and protected data.
Beyond Data Warehousing: Big Data Engineering
While talking about data engineering, we cannot take no notice of the big data concept. Deal with the three Vs (velocity, volume, and variety) big data commonly floods big technology enterprises like Amazon, YouTube, or Instagram. Big Data Engineering is all about constructing huge reservoirs and extremely scalable and continue operating without interruption distributed systems capable to integrally store and process data.
Big data architecture varies from the conventional data control, such as here we are speaking about such huge volumes of fast changing information streams that a data warehouse is not capable to accommodate. The structural design that can hold and process such huge amount of data is a data lake.
Data lake architecture
A data lake is a storage warehouse (repository) that handle and process a massive amount of raw data in its native format up until it is required. While a hierarchical data warehouse save data in folders or files, a flat architecture is used by a data lake to store data.
The ELT approach is used by a data lake exchanging, transform and to load operations in the normal ETL operations sequence. Supporting huge storing and scalable computing, a data lake begins data loading instantly after extracting it. This lets for increasing sizes of data to be handled. A data lake is very suitable.
Hadoop platform – a hands-on example of a data lake
Hadoop is a widely held open source example of a data lake platform. Based on Java, it is a large-scale and extensive data processing framework. This software project is able to structure many big data types for more analysis. The platform permits to split data analysis tasks across many computers and handling them in parallel.
The Hadoop ecosystem contains some set of tools that are following.
Hadoop Distributed File System (HDFS).
HDFS comprises two modules: Name-Node save metadata while Data-Node store for actual data and accomplishes operations according to Name-Node.
MapReduce is a framework for scripting applications that practice the data saved in HDFS. MapReduce programs are good to perform big data analysis using several machines in the cluster.
YARN assists to monitor and manage workloads.
A system for querying, summarizing and analyzing big datasets, Hive practices its own language known as HQL which is alike to SQL. HiveQL automatically transforms queries, SQL-like, into MapReduce tasks for implementation on Hadoop.
Having same objectives as Hive, it also has its own language stated as PigLatin. When to practice Pig and when to practice Hive is the confusion. Pig is a better choice for programming commitments, while Hive is mostly castoff by data analysts for generating reports.
A NoSQL database made on top of HDFS that delivers real-time access to write or read data.
There are a lot of other components that enable Hadoop functionality: Avro, HCatalog, Thrift, Mahout , Apache Drill, Sqoop, Ambari ,Flume, Oozie, Zookeeper, etc.
Tools for writing ETL pipelines:
This was firstly developed by Airbnb to re-architect their data pipelines. Transferring to Airflow, it lessened their run-time of experimentation reporting framework (ERF) from 120 minutes to about 45 minutes. The key feature of Airflow is automating scripts to do jobs.
A cloud-based data processing deal, Dataflow is defined at extensive data ingestion and low-latency processing through wild parallel accomplishment of the analytics pipelines. Dataflow has an advantage over Airflow, as it follows many languages like Python, Java, SQL, and engines like Spark and Flink. It is also well keep up by Google Cloud.
Apache Kafka Allots data across many nodes for a highly available deployment, from a messaging queue to a full-fledged event streaming platform, inside a sole data center or across various availability areas. It also provides durable storage.
Read more about docker https://blogs.slimlogix.com/a-deep-introduction-of-docker-and-its-architecture/