This chapter is an overview of big data and Hive, especially in the Hadoop ecosystem. It briefly introduces the evolution of big data so that readers know where they are in the journey of big data and can find out their preferred areas in future learning. This chapter also covers how Hive has become one of the leading tools in the big data ecosystem and why it is still competitive.
In this chapter, we will cover the following topics:
- A short history from the database, data warehouse to big data
- Introducing big data
- Relational and NoSQL databases versus Hadoop
- Batch, real-time, and stream processing
- Hadoop ecosystem overview
- Hive overview
In the 1960s, when computers became a more cost-effective option for businesses, people started to use databases to manage data. Later on, in the 1970s, relational databases became more popular for business needs since they connected physical data with the logical business easily and closely. In the next decade, Structured Query Language (SQL) became the standard query language for databases. The effectiveness and simplicity of SQL motivated lots of people to use databases and brought databases closer to a wide range of users and developers. Soon, it was observed that people used databases for data application and management and this continued for a long period of time.
Once plenty of data was collected, people started to think about how to deal with the historical data. Then, the term data warehousing came up in the 1990s. From that time onward, people started discussing how to evaluate current performance by reviewing the historical data. Various data models and tools were created to help enterprises effectively manage, transform, and analyze their historical data. Traditional relational databases also evolved to provide more advanced aggregation and analyzed functions as well as optimizations for data warehousing. The leading query language was still SQL, but it was more intuitive and powerful compared to the previous versions. The data was still well-structured and the model was normalized. As we entered the 2000s, the internet gradually became the topmost industry for the creation of the majority of data in terms of variety and volume. Newer technologies, such as social media analytics, web mining, and data visualizations, helped lots of businesses and companies process massive amounts of data for a better understanding of their customers, products, competition, and markets. The data volume grew and the data format changed faster than ever before, which forced people to search for new solutions, especially in the research and open source areas. As a result, big data became a hot topic and a challenging field for many researchers and companies.
However, in every challenge there lies great opportunity. In the 2010s, Hadoop, which was one of the big data open source projects, started to gain wide attention due to its open source license, active communities, and power to deal with the large volumes of data. This was one of the few times that an open source project led to the changes in technology trends before any commercial software products. Soon after, the NoSQL database, real-time analytics, and machine learning, as followers, quickly became important components on top of the Hadoop big data ecosystem. Armed with these big data technologies, companies were able to review the past, evaluate the current, and grasp the future opportunities.
Big Data is not simply a big volume of data. Here, the word Big refers to the big scope of data. A well-known saying in this domain is to describe big data with the help of three words starting with the letter V: volume, velocity, and variety. But the analytical and data science world has seen data varying in other dimensions in addition to the fundament three Vs of big data, such as veracity, variability, volatility, visualization, and value. The different Vs mentioned so far are explained as follows:
- Volume: This refers to the amount of data generated in seconds. 90% of the world's data today has been created in the last two years. Since that time, the data in the world doubles every two years. Such big volumes of data are mainly generated by machines, networks, social media, and sensors, including structured, semi-structured, and unstructured data.
- Velocity: This refers to the speed at which the data is generated, stored, analyzed, and moved around. With the availability of internet-connected devices, wireless or wired machines and sensors can pass on their data as soon as it is created. This leads to real-time data streaming and helps businesses to make valuable and fast decisions.
- Variety: This refers to the different data formats. Data used to be stored in the .txt, .csv, and .dat formats from data sources such as filesystems, spreadsheets, and databases. This type of data, which resides in a fixed field within a record or file, is called structured data. Nowadays, data is not always in the traditional structured format. The newer semi-structured or unstructured forms of data are also generated by various methods such as email, photos, audio, video, PDFs, SMSes, or even something we have no idea about. These varieties of data formats create problems for storing and analyzing data. This is one of the major challenges we need to overcome in the big data domain.
- Veracity: This refers to the quality of data, such as trustworthiness, biases, noise, and abnormality in data. Corrupted data is quite normal. It could originate due to a number of reasons, such as typos, missing or uncommon abbreviations, data reprocessing, and system failures. However, ignoring this malicious data could lead to inaccurate data analysis and eventually a wrong decision. Therefore, making sure the data is correct in terms of data audition and correction is very important for big data analysis.
- Variability: This refers to the changing of data. It means that the same data could have different meanings in different contexts. This is particularly important when carrying out sentiment analysis. The analysis algorithms are able to understand the context and discover the exact meaning and values of data in that context.
- Volatility: This refers to how long the data is valid and stored. This is particularly important for real-time analysis. It requires a target time window of data to be determined so that analysts can focus on particular questions and gain good performance out of the analysis.
- Visualization: This refers to the way of making data well understood. Visualization does not only mean ordinary graphs or pie charts; it also makes vast amounts of data comprehensible in a multidimensional view that is easy to understand. Visualization is an innovative way to show changes in data. It requires lots of interaction, conversations, and joint efforts between big data analysts and business-domain experts to make the visualization meaningful.
- Value: This refers to the knowledge gained from data analysis on big data. The value of big data is how organizations turn themselves into big data-driven companies and use the insight from big data analysis for their decision-making.
In summary, big data is not just about lots of data, it is a practice to discover new insight from existing data and guide the analysis of new data. A big-data-driven business will be more agile and competitive to overcome challenges and win competitions.
To better understand the differences among the relational database, NoSQL database, and Hadoop, let's compare them with ways of traveling. You will be surprised to find that they have many similarities. When people travel, they either take cars or airplanes, depending on the travel distance and cost. For example, when you travel to Vancouver from Toronto, an airplane is always the first choice in terms of the travel time versus cost. When you travel to Niagara Falls from Toronto, a car is always a good choice. When you travel to Montreal from Toronto, some people may prefer taking a car to an airplane. The distance and cost here are like the big data volume and investment. The traditional relational database is like the car, and the Hadoop big data tool is like the airplane. When you deal with a small amount of data (short distance), a relational database (like the car) is always the best choice, since it is fast and agile to deal with a small or moderate amount of data. When you deal with a big amount of data (long distance), Hadoop (like the airplane) is the best choice, since it is more linear-scalable, fast, and stable to deal with the big volume of data. You could drive from Toronto to Vancouver, but it takes too much time. You can also take an airplane from Toronto to Niagara Falls, but it would take more time on your way to the airport and cost more than traveling by car. In addition, you could take a ship or a train. This is like a NoSQL database, which offers characteristics and balance from both a relational database and Hadoop in terms of good performance and various data format support for moderate to large amounts of data.
Batch processing is used to process data in batches. It reads data from the input, processes it, and writes it to the output. Apache Hadoop is the most well-known and popular open source implementation of the distributed batch processing system using the MapReduce paradigm. The data is stored in a shared and distributed file system, called Hadoop Distributed File System (HDFS), and divided into splits, which are the logical data divisions for MapReduce processing.
To process these splits using the MapReduce paradigm, the map task reads the splits and passes all of its key/value pairs to a map function, and writes the results to intermediate files. After the map phase is completed, the reducer reads intermediate files sent through the shuffle process and passes them to the reduce function. Finally, the reduce task writes results to the final output files. The advantages of the MapReduce model include making distributed programming easier, near-linear speed-up, good scalability, as well as fault tolerance. The disadvantage of this batch processing model is being unable to execute recursive or iterative jobs. In addition, the obvious batch behavior is that all input must be ready by map before the reduce job starts, which makes MapReduce unsuitable for online and stream-processing use cases.
Real-time processing is used to process data and get the result almost immediately. This concept in the area of real-time ad hoc queries over big data was first implemented in Dremel by Google. It uses a novel columnar storage format for nested structures with fast index and scalable aggregation algorithms for computing query results in parallel instead of batch sequences. These two techniques are the major characters for real-time processing and are used by similar implementations, such as Impala (https://impala.apache.org/), Presto (https://prestodb.io/), and Drill (https://drill.apache.org/), powered by the columnar storage data format, such as Parquet (https://parquet.apache.org/), ORC (https://orc.apache.org/), CarbonData (https://carbondata.apache.org/), and Arrow (https://arrow.apache.org/). On the other hand, in-memory computing no doubt offers faster solutions for real-time processing. In-memory computing offers very high bandwidth, which is more than 10 gigabytes/second, compared to a hard disk's 200 megabytes/second. Also, the latency is comparatively lower, nanoseconds versus milliseconds, compared to hard disks. With the price of RAM getting lower and lower each day, in-memory computing is more affordable as a real-time solution, such as Apache Spark (https://spark.apache.org/), which is a popular open source implementation of in-memory computing. Spark can be easily integrated with Hadoop, and its in-memory data structure Resilient Distributed Dataset (RDD) can be generated from data sources, such as HDFS and HBase, for efficient caching.
Stream processing is used to continuously process and act on the live stream data to get a result. In stream processing, there are two commonly used general-purpose stream processing frameworks: Storm (https://storm.apache.org/) and Flink (https://flink.apache.org/). Both frameworks run on the Java Virtual Machine (JVM) and both process keyed streams. In terms of the programming model, Storm gives you the basic tools to build a framework, while Flink gives you a well-defined and easily used framework. In addition, Samza (http://samza.apache.org/) and Kafka Stream (https://kafka.apache.org/documentation/streams/) leverage Kafka for both message-caching and transformation. Recently, Spark also provides a type of stream processing in terms of its innovative continuous-processing mode.
Hadoop was first released by Apache in 2011 as Version 1.0.0, which only contained HDFS and MapReduce. Hadoop was designed as both a computing (MapReduce) and storage (HDFS) platform from the very beginning. With the increasing need for big data analysis, Hadoop attracts lots of other software to resolve big data questions and merges into a Hadoop-centric big data ecosystem. The following diagram gives a brief overview of the Hadoop big data ecosystem in Apache stack:
Hive is a standard for SQL queries over petabytes of data in Hadoop. It provides SQL-like access to data in HDFS, enabling Hadoop to be used as a data warehouse. The Hive Query Language (HQL) has similar semantics and functions as standard SQL in the relational database, so that experienced database analysts can easily get their hands on it. Hive's query language can run on different computing engines, such as MapReduce, Tez, and Spark.
Hive's metadata structure provides a high-level, table-like structure on top of HDFS. It supports three main data structures, tables, partitions, and buckets. The tables correspond to HDFS directories and can be divided into partitions, where data files can be divided into buckets. Hive's metadata structure is usually the Schema of the Schema-on-Read concept on Hadoop, which means you do not have to define the schema in Hive before you store data in HDFS. Applying Hive metadata after storing data brings more flexibility and efficiency to your data work. The popularity of Hive's metadata makes it the de facto way to describe big data and is used by many tools in the big data ecosystem.
The following diagram is the architecture view of Hive in the Hadoop ecosystem. The Hive metadata store (also called the metastore) can use either embedded, local, or remote databases. The thrift server is built on Apache Thrift Server technology. With its latest version 2, hiveserver2 is able to handle multiple concurrent clients, support Kerberos, LDAP, and custom pluggable authentication, and provide better options for JDBC and ODBC clients, especially for metadata access.
Here are some highlights of Hive that we can keep in mind moving forward:
- Hive provides a simple and optimized query model with less coding than MapReduce
- HQL and SQL have a similar syntax
- Hive's query response time is typically much faster than others on the same volume of big datasets
- Hive supports running on different computing frameworks
- Hive supports ad hoc querying data on HDFS and HBase
- Hive supports user-defined java/scala functions, scripts, and procedure languages to extend its functionality
- Matured JDBC and ODBC drivers allow many applications to pull Hive data for seamless reporting
- Hive allows users to read data in arbitrary formats, using SerDes and Input/Output formats
- Hive is a stable and reliable batch-processing tool, which is production-ready for a long time
- Hive has a well-defined architecture for metadata management, authentication, and query optimizations
- There is a big community of practitioners and developers working on and using Hive
After going through this chapter, we are now able to understand when and why to use big data instead of a traditional relational database. We also learned about the difference between batch processing, real-time processing, and stream processing. We are now familiar with the Hadoop ecosystem, especially Hive. We have traveled back in time and brushed through the history of databases, data warehouse, and big data. We also explored some big data terms, the Hadoop ecosystem, the Hive architecture, and the advantage of using Hive.
In the next chapter, we will practice installing Hive and review all the tools needed to start using Hive in the command-line environment.