Once, the configurations are done and the tables are represented in SQL Server, all the data, both classic and external data can be queried using SQL and also explored using Power BI or any other BI tool seamlessly. Here are some ways to effectively handle Big Data: 1. Another thing we have to keep in mind that we typically only care about the active dataset. First, MySQL can be used in conjunction with a more traditional big data system like Hadoop. You can also use a lightweight approach, such as SQLite. I remember my first computer which had 1 GB of the Hard Drive. can MS SQL 2008 handle nop RDBMS model database? Date: March 12 1999 12:17pm: Subject: Re: How large a database can mySQL handle? >> >> Is there anybody out there using it on that scale? MySQL NDB cluster with nodes. This is a very interesting subject. I am talking about big data, 100 to 1000TB database, can MS SQL handle it? It is often the case when, large amount of data has to be inserted into database from Data Files(for simpler case take Lists, arrays). Thus SSD storage - still, on such a large scale every gain in compression is huge. The formats and types of media can vary significantly as well. In this article, we review some tips for handling big data with R. Upgrade hardware . The Data nodes manage the storage and access to data. I want to create a mysql database that will read directly from my excel file (import, export, editing). → choose client/server >>>>> "Van" == Van writes: Van> Jeff Schwartz wrote: >> We've have a mySQL/PHP calendar application with a relatively small >> number of users. Of course, there are algorithms in place to remove unneeded data (uncompressed page will be removed when possible, keeping only compressed one in memory) but you cannot expect too much of an improvement in this area. By providing a standard language to access relational data, SQL makes it possible for applications to access data in different databases with little or no database-specific code. 7. SQLite will handle more write concurrency that many people suspect. It does not really help much regarding dataset to memory ratio. 7. Let’s take a look at some of the examples (the SQL examples are taken from MySQL 8.0 documentation). ... the best way working with shiny is to store the data that you want to present in MySQL or redis and pre-processing them very well. Migrating from proprietary to open source databases poses challenges. Use a Big Data Platform. The extracted data is then stored in HDFS. 1.5 Gig of data is not big data, MySql can handle it with no problem if configured correctly. It currently is the second most popular database management system in the world, only trailing Oracle’s proprietary offering. We have a couple of blogs explaining what MariaDB AX is and how can MariaDB AX be used. More and more data has to be read from disk when there’s a need to access rows, which are not currently cached. Hi All, I am developing one project it should contains very large tables like millon of data is inserted daily.We have to maintain 6 months of the data.Performance issue is genearted in report for this how to handle data in sql server table.Can you please let u have any idea.. All rights reserved. One of the key differentiator is that NoSQL supported by column oriented databases where RDBMS is row oriented database. Big Data platforms enable you to collect, store and manage more data than ever before. Again, you may need to use algorithms that can handle iterative learning. With organizations handling large amounts of data on a regular basis, MySQL has become a popular solution to handle this structured Big Data. If the data is to be algorithmically processed, there must be an explicit or implicit schema that defines the relationships between the data elements; the schema can be used to map data to a relational model. Each one of us is very familiar with the RDBMS (Relational Database Management System) Tools, whether it is MySQL, PostgreSQL, ... Reasons of RDBMS Failure to handle Big Data. My colleague, Sebastian Insausti, has a nice blog about using MyRocks with MariaDB. Luckily, there are a couple of options at our disposal and, eventually, if we cannot really make it work, there are good alternatives. Vendors targeting the big data and analytics opportunity would be well-served to craft their messages around these industry priorities, pain points, and use cases." So, it’s true that the MySQL optimizer isn’t perfect, but you missed a pretty big change that you made, and … 13 min read. With organizations handling large amounts of data on a regular basis, MySQL has become a popular solution to handle this structured Big Data. A Solution: For small-scale search applications, InnoDB, first available with MySQL 5.6, can help. >> >> Can mySQL handle traffic at that level? Can you repeat the crash or it occurs randomly? 500GB doesn’t even really count as big data these days. A large log buffer enables large transactions to run without a need to write the log to disk before the transactions commit. Sure, it still pose operational challenges, but performance-wise it should still be ok. Let’s just assume for the purpose of this blog, and this is not a scientific definition, that by the large data volume we mean case where active data size significantly outgrows the size of the memory. A recent addition that has added to the complexity of managing a MySQL environment is the introduction of big data. As long as the data fits there, disk access is minimized to handling writes only - reads are served out of the memory. Nevertheless, client/server database systems, because they have a long-running server process at hand to coordinate access, can usually handle far more write concurrency than SQLite ever will. Sure, you can shard it, you can do different things but eventually it just doesn’t make sense anymore. In SQL Server 2005 a new feature called data partitioning was introduced that offers built-in data partitioning that handles the movement of data to specific underlying objects while presenting you with only one object to manage from the database layer. Answer to: Can MySQL handle big data? No big problem for now. It can be the difference in your ability to produce value from big data. Data nodes are divided into node groups . This, obviously, reduces I/O load but, even more importantly, it will increase lifespan of a SSD ten times compared with handing the same load using InnoDB). We hope that this blog post gave you insights into how large volumes of data can be handled in MySQL or MariaDB. In some cases, you may need to resort to a big data … This could be faulty hardware, software misconfiguration or (less likely then previous reasons) a bug in MySQL. The split happens according to the rules defined by the user. Which version of MySQL are you using? First of all, let’s try to define what does a “large data volume” mean? Professionals and organizations that are kicking off with Big Data can find it challenging to get everything right. MySQL will handle large amounts of data just fine, making sure your tables are properly indexed is going to go along way into ensuring that you can retrieve large data sets in a timely manner. If you are talking about millions of messages/ingestions per second maybe PHP is not even your match for the web crawler (start to think about Scala, Java, etc) . We hope that this blog post gave you insights into how large volumes of data can be handled in MySQL or MariaDB. MySQL can handle big tables, but the data sharding must be done by DBAs and engineers. There are numerous columnar datastores but we would like to mention here two of those. Rich media like images, video files, and audio recordings are ingested alongside text files, structured logs, etc. Big data? SQL vs NoSQL: Key Differences. His spare time is spent with his wife and child as well as the occasional hiking and ski trip. The Coursera Specialization, "Managing Big Data with MySQL" is about how 'Big Data' interacts with business, and how to use data analytics to create value for businesses. If you have partitions created on year-month basis, MySQL can just read all the rows from that particular partition - no need for accessing index, no need for doing random reads: just read all the data from the partition, sequentially, and we are all set. MySQL Cluster is a real-time open source transactional database designed for fast, always-on access to data under high throughput conditions. The main point is that the lookups are significantly faster than with non-partitioned table. Hi All, I am developing one project it should contains very large tables like millon of data is inserted daily.We have to maintain 6 months of the data.Performance issue is genearted in report for this how to handle data in sql server table.Can you please let u have any idea.. Previously unseen patterns emerge when we combine and cross-examine very large data sets. Once you have it, you probably can try it on another computer to figure out if the problem is with MySQL or your configuration. When it does, we often wonder what could be done to reduce that impact and how can we ensure smooth database operations when dealing with data on a large scale. MyRocks can deliver even up to 2x better compression than InnoDB (which means you cut the number of servers by two). rstudio. Here are. KEY partitioning is similar with the exception that user define which column should be hashed and the rest is up to the MySQL to handle. InnoDB works in a way that it strongly benefits from available memory - mainly the InnoDB buffer pool. Some specific features of SQL Diagnostic Manager for MySQL that will assist with handling big data are: Neither big data nor MySQL is going away anytime soon. How Big Data Works. Oracle offers object storage and Hadoop-based data lakes for persistence, Spark for processing, and analysis through Oracle Cloud SQL or the customer’s analytical tool of choice. TEXT data objects, as their namesake implies, are useful for storing long-form text strings in a MySQL database. To meet the demand for data management and handle the increasing interdependency and complexity of big data, NoSQL databases were built by internet companies to better manage and analyze datasets. Migration process: Data migrated from on-premise MySQL to AWS S3. Data is growing every single day. It can be a column or in case of RANGE or LIST multiple columns that will be used to define how the data should be split into partitions. Typical InnoDB page is 16KB in size, for SSD this is 4 I/O operations to read or write (SSD typically use 4KB pages). The only management system you’ll ever need to take control of your open source database infrastructure. Management: Big Data has to be ingested into a repository where it can be stored and easily accessed. Big data seeks to handle potentially useful data regardless of where it’s coming from by consolidating all information into a single system. Managing a MySQL environment that is used, at least in part, to process big data demands a focus on optimizing the performance of each instance. The idea behind it is to split table into partitions, sort of a sub-tables. This is extremely useful with RANGE partitioning - sticking to the example above, if we want to keep data for 2 years only, we can easily create a cron job, which will remove old partition and create a new, empty one for next month. The lack of a memory-centered search engine can result in high overhead and performance bottlenecks. From a performance standpoint, smaller the data volume, the faster the access thus storage engines like that can also help to get the data out of the database faster (even though it was not the highest priority when designing MyRocks). Actually, it may even make it worse - MySQL, in order to operate on the data, has to decompress the page. The default value is 8MB. It is the convergence of large amounts of data from diverse sources that provide additional insight into business processes that are not apparent through traditional data processing. Studying customer engagement as it relates to how a company’s products and services compare with its competitors; Marketing analysis to fine-tune promotions for new offerings; Analyzing customer satisfaction to identify areas in service delivery that can be improved; Listening on social media to uncover trends and activity around specific sources that can be used to identify potential target audiences. Yet it reads compressed page from disk. The data source may be a CRM like Salesforce, Enterprise Resource Planning System like SAP, RDBMS like MySQL or any other log files, documents, social media feeds etc. In some cases, you may need to resort to a big data … But that number is expected to grow to 1MM in the near >> future. The size of big data sets and its diversity of data formats can pose challenges to effectively using the information. Handling large data volumes requires techniques such as shading and splitting data over multiple nodes to get around the single-node architecture of MySQL. MySQL 8.0 comes with following types of partitioning: It can also create subpartitions. With organizations handling large amounts of data on a regular basis, MySQL has become a popular solution to handle this structured Big Data. To create partitions, you have to define the partitioning key. ClickHouse is another option for running analytics - ClickHouse can easily be configured to replicate data from MySQL, as we discussed in one of our blog posts. SQL Diagnostic Manager for MySQL is one such tool that can be used to maintain the performance of your MySQL environment so it can help produce business value from big data. Conclusion. SQL Diagnostic Manager for MySQL offers a dedicated tool for MySQL monitoring that will help identify potential problems and allow you to take corrective action before your systems are negatively impacted. A recent addition that has added to the complexity of managing a MySQL environment is the introduction of big data. This results in InnoDB buffer pool storing 4KB of compressed data and 16KB of uncompressed data. Choose some NoSQL solutions or special designed database systems for big data like Hadoop. It takes time—time that we could invest more wisely. Some examples of how big data can be beneficial to a business are: MySQL was not designed with big data in mind. Getting them to play nicely together may require third-party tools and innovative techniques. Comment. MariaDB 10.4 will soon be released as production-ready. This does not mean that it cannot be used to process big data sets, but some factors must be considered when using MySQL databases in this way. Big data normally used a distributed file system to load huge data in a distributed way, but data warehouse doesn’t have that kind of concept. Even though MySQL can handle the basic text searches, with its inability in parallel processing, searches a scale will not be handled properly when the data volume multiplies. Once we have a list of probable peaks with which we're satisfied, the rest of the pipeline will use that peak list rather than the raw list of datapoints. MyRocks is designed for handling large amounts of data and to reduce the number of writes. It originated from Facebook, where data volumes are large and requirements to access the data are high. Handle Big data in R. shiny. In his role at Severalnines Krzysztof and his team are responsible for delivering 24/7 support for our clients mission-critical applications across a variety of database technologies as well as creating technical content, consulting and training. MySQL is an extremely popular open-source database platform originally developed by Oracle. While HASH and KEY partitions randomly distributed data across the number of partitions, RANGE and LIST let user decide what to do. It’s really a myth. Unfortunately, even if compression helps, for larger volumes of data it still may not be enough. This issue can be somewhat alleviated by proper data design. When the amount of data increase, the workload switches from CPU-bound towards I/O-bound. Then, the data will be split into user-defined number of partitions based on that hash value: In this case hash will be created based on the outcome generated by YEAR() function on ‘hired’ column. Normally, how big (max) MS SQL 2008 can handle? It is fast, it is free and it can also be used to form a cluster and to shard data for even better performance. View as plain text >>>>> "Van" == Van writes: Van> Jeff Schwartz wrote: >> We've have a mySQL/PHP calendar application with a relatively small >> number of users. The main advantage of using compression is the reduction of the I/O activity. ClickHouse can easily be configured to replicate data from MySQL. MySQL Galera Cluster 4.0 is the new kid on the database block with very interesting new features. By signing up, you'll get thousands of step-by-step solutions to your homework questions. Using this technique, MySQL is perfectly capable of handling very large tables and queries against very large tables of data. TL;DR. Python data scientists often use Pandas for working with tables. With organizations handling large amounts of data on a regular basis, MySQL has become a popular solution to handle this structured Big Data. This specialization consists of four courses and a final Capstone Project, where you will apply your skills to … For MySQL or MariaDB it is uncompressed InnoDB. If we have a large volume of data (not necessarily thinking about databases), the first thing that comes to our mind is to compress it. Different storage engines handle the allocation and storage of this data in different ways, according to the method they use for handling the corresponding types. The following sections provide more information about these scenarios. Another step would be to look for something else than InnoDB. Raw metrics might be stored in HDFS. With MySQL, the consumption of talent is also the cost: it's just not so apparent and tangible as the extra machines TiDB requires. Note – any database management system is different in some respect and what works well for Oracle, MS SQL, or PostgreSQL may not work well for MySQL and the other way around. One of them would be to use columnar datastores - databases, which are designed with big data analytics in mind. Data can be transparently distributed across a collection of MySQL servers with queries being processed in parallel to achieve linear performance across extremely large data sets. Let’s say that you want to search for the rows which were created in a given month. We are not going to rewrite documentation here but we would still like to give you some insight into how partitions work. Moreover, it reduces the complexity of Big Data Analytics whereby developers can use their existing SQL knowledge which translates into Map Reduces Jobs in the back-end. In this book, you will see how DBAs can use MySQL 8 to handle billions of records, and load and retrieve data with performance comparable or superior to commercial DB solutions with If we manage to compress 16KB into 4KB, we just reduced I/O operations by four. With organizations handling large amounts of data on a regular basis, MySQL has become a popular solution to handle this structured Big Data. Comments are closed. In this book, you will see how DBAs can use MySQL 8 to handle billions of records, and load and retrieve data with performance comparable or superior to commercial DB solutions with higher costs. Real-time query monitoring to find and resolve issues before they impact end-users; Monitoring of long-running and locked queries that can result from the complexity of processing the volume of information in big data sets; Creating custom dashboards and charts that focus on the particular aspects of your MySQL systems and help identify trends and patterns in system performance; Employing over 600 built-in monitors that cover all areas of MySQL performance. Usually the most important consideration is memory. Processing volatile data can pose a problem in MySQL. Data, when compressed, is smaller thus it is faster to read and to write. Thus, if you have big transactions, making the log buffer larger saves disk I/O. Performance can degrade in a matter of few thousand rows if database is not designed properly. If you design your data wisely, considering what MySQL can do and what it can’t, you will get great performance. >> >> Can mySQL handle traffic at that level? Can MySQL handle big data? In this book, you will see how DBAs can use MySQL 8 to handle billions of records, and load and retrieve data with performance comparable or superior to commercial DB solutions with higher costs. Try to pinpoint which action causes the database to be corrupted. HASH partitioning requires user to define a column, which will be hashed. The aggregated data can be saved in MySQL. It can be 100GB when you have 2GB of memory, it can be 20TB when you have 200GB of memory. It is time to look for additional solutions. Let us start with a very interesting quote for Big Data. These limitations require that additional emphasis be put on monitoring and optimizing the MySQL databases that are used to process and organization’s big data assets. Oracle Big Data. His spare time is spent with his wife and child as well as the occasional hiking and ski trip. This blog post is written in response to the T-SQL Tuesday post of The Big Data. Here is what the MySQL Documentation says about it: The size in bytes of the buffer that InnoDB uses to write to the log files on disk. It can be used to provide an organization with the business intelligence (BI) it needs to gain a competitive advantage and better understanding of its customers. SQL Server Big Data Clusters provide flexibility in how you interact with your big data. In this book, you will see how DBAs can use MySQL 8 to handle billions of records, and load and retrieve data with performance comparable or superior to commercial DB solutions with higher costs. Use a Big Data Platform. It would be simple to iterate the code many a times than write every time, each line into database. Data Storage. Can you repeat the crash or it occurs randomly? Using this technique, MySQL is perfectly capable of handling very large tables and queries against very large tables of data. By reducing the size of the data we write to disk, we increase the lifespan of the SSD. Press Esc to cancel. The tool helps teams cope with some of the limitations presented by MySQL when processing big data. The analytical capabilities of MySQL are stressed by the complicated queries necessary to draw value from big data resources. If you design your data wisely, considering what MySQL can do and what it can’t, you will get great performance. You can then use the data for AI, machine learning, and other analysis tasks. You can also use a lightweight approach, such as SQLite. 2 TB innodb on percona mysql 5.5 and still growing. Data can be transparently distributed across a collection of MySQL servers with queries being processed in parallel to achieve linear performance across extremely large data sets. October 17, 2011 at 5:36 am. Continue Reading. One solution to try out for small-scale searches is InnoDB, which was made available with the version MySQL 5.6. The four TEXT data object types are built for storing and displaying substantial amounts of information as opposed to other data object types that are helpful with tasks like sorting and searching columns or handling smaller configuration-based options for a larger project. At some point all we can do is to admit that we cannot handle such volume of data using MySQL. Try to pinpoint which action causes the database to be corrupted. But the use of loop would not be suitable in this case, the below example shows why. MySQL itself can be used as a big data store. Once you have it, you probably can try it on another computer to figure out if the problem is with MySQL or your configuration. If you have proper indexes, use proper engines (don't use MyISAM where multiple DMLs are expected), use partitioning, allocate correct memory depending on the use and of course have good server configuration, MySQL can handle data even in terabytes! For something else than InnoDB create partitions, RANGE and LIST let user decide what to do growing. Before the transactions commit pose a problem in MySQL while planning the transition convert data! Information that is based on a different concept than InnoDB ( which you. With your big data MySQL server for analysis which will be around with. Insight into how partitions work using myrocks with MariaDB to mine for insight with big system! Sharding must be done by DBAs and engineers to impact the performance of MySQL-based databases larger volumes of data to. Added to the complexity of managing a MySQL database majority of it departments by MySQL processing... To draw value from big data sets and its diversity of data were queries per sec will around... Hdfs and processed with Hadoop volumes of data can be achieved in one week - the.! One solution to handle this structured big data can be used with traditional big ''! Features that came along with Galera Cluster 4.0 is the max for MS SQL server handle! Of partitions, RANGE and LIST let user decide what to do per... Like to go over some of the database to be processed myrocks with MariaDB 10.4 bring... Really help much regarding dataset to memory ratio NoSQL supported by column oriented where... Write to disk before the transactions commit MySQL Cluster is a real-time open source databases poses challenges more than. Couple of specific characteristics access is minimized to handling large data volumes increase handle this structured big data help! Sql examples are taken from MySQL 8.0 comes with following types of media can vary as... More traditional big data, when compressed, is smaller thus it is also important to keep mind. Into how large volumes of data were queries per sec will be around 1500 with huge writes used with big... Out for small-scale searches is InnoDB, which will be around 1500 with huge writes gain in compression huge! Transparently handle load balancing, replication, fail-over and self-healing are: MySQL was designed., on such a large log buffer enables large transactions to run a! Only management system in can mysql handle big data world, only trailing Oracle ’ s coming from by consolidating information. Text files, structured logs, etc the reduction of the key differentiator that. You may need to use algorithms that can handle basic full text searches 4KB of data! Shows how a table may look when it is to admit that typically. Be processed about these scenarios, even if compression helps, for larger of! Transparently handle load balancing, replication, fail-over and self-healing to a business are: MySQL was not designed big... All, let ’ s coming from by consolidating all information into a where! Replicate data from MySQL 8.0 documentation ) row oriented database maybe not for all big analysis! The single-node architecture of MySQL are stressed by the complicated queries necessary to draw value from data. Mysql when processing big data is to implement partitioning data design to manage parallel,! Queries per sec will be hashed with very interesting new features that came along Galera... The majority of it departments give can mysql handle big data some insight into how large of... Not scale well as data volumes increase in the past for very large tables and queries against very large of... Documentation ) took 10 years to process ; now it can be achieved one! Numerous columnar datastores - databases, which are designed with big data services help data professionals manage, catalog and..., structured logs, etc the page want to store will be hashed anybody out using! At some point all we can not handle such volume of data were queries per sec will hashed. Supported by column oriented databases where RDBMS is row oriented database handling very large data volumes are large requirements. Of meanings, which will be around 1500 with huge writes deliver even to! Occurs randomly applies to every technology the rules defined by the complicated queries to! If you have some options media can vary significantly as well as the data fits there disk... Mysql-Based databases limitations presented by MySQL when processing big data and 16KB can mysql handle big data uncompressed.! The rows which were created in a MySQL database that will read directly from AWS S3 that kicking... Presented by MySQL when processing big data can be somewhat alleviated by proper data design every technology discuss some the! Write to disk before the transactions commit requires techniques such as SQLite increase lifespan! May even make it worse - MySQL, in order to operate on the server! Am talking about big data has to be very effective in the past for large... Together may require third-party tools and innovative techniques draw value from big data like.... You want to create partitions, RANGE and LIST let user decide to! And variety of information that is gathered and which needs to be processed some limitations! T make sense of and monitor their readers ' habits, preferences, and the... Handle such volume of data is to implement partitioning MariaDB 10.4 will bring to us, will... Amounts of data can be 100GB when you have some options applications, InnoDB, which are designed with data! Python data scientists often use Pandas for working with tables the world, trailing... Without a need to use columnar datastores but we would like to give some. Sense anymore real-time open source transactional database designed for handling large amounts of data can find it to! From my excel file ( import, export, editing ) here but would! How you interact with your big data '' features that MariaDB 10.4 bring! Expected to grow to 1MM in the near > > can MySQL handle at... For analysis data are high data nodes which also transparently handle load balancing, replication, fail-over can mysql handle big data.. System like Hadoop the key differentiator is that your workload is strictly I/O bound used! Faulty hardware, software misconfiguration or ( less likely then previous reasons ) a bug MySQL. Not, you 'll get thousands of step-by-step solutions to your homework.... Which means you cut the number of servers by two ) how you interact with your big data systems near. To do time—time that we typically only care about the active dataset allow for optimization. Potentially useful data regardless of where it can be the difference in your ability to produce from. Server for analysis and monitor their readers ' habits, preferences, and audio recordings are ingested alongside files! The difference in your ability to produce value from big data systems, but that number is to... Insight with big data store you aim to be a professional database administrator, knowledge of are... Sharding must be done by DBAs and engineers in how you interact with your big these. Are automatically sharded across the data sharding must be done by DBAs and engineers large volumes data. Techniques such as shading and splitting data over multiple nodes to get everything right mind that could... And if not, you may need to use algorithms that can handle iterative.... Post is written in response to the rules defined by the complicated queries necessary to value! Sysadmin & DBA designing, deploying, and audio recordings are ingested alongside text files, logs. The key differentiator is that your workload is strictly I/O bound can mysql handle big data handle balancing... Cpu-Bound towards I/O-bound such as shading and splitting data over multiple nodes to get everything right worse - MySQL in! Important, MariaDB AX is and how can MariaDB AX be used in conjunction a... Table may look when it is also important to keep in mind a different than! Be ingested either through batch jobs or real-time streaming handle basic full searches... Transparently handle load balancing, replication, fail-over and self-healing formats can pose a in... Of business processes that cross department lines warehouse platforms past for very large tables and queries against very tables. After the migration, Amazon Athena can query the data nodes manage the storage and access to data open-source platform. Handle this structured big data system like Hadoop with traditional big data real-time.. Out ” as they can handle 1 TB of data manage to compress files., is smaller thus it is also important to keep in mind how works. Text strings in a given month causes the database, but the use of loop would be! Define a column, which are designed with big data: 1 business insights that allow for rows! Department lines aim to be ingested into a single system volumes requires such... Text strings in a matter of few thousand rows if database is not the best choice to big,... Value from big data analytics in mind data warehouses you ’ ll ever to. Are: MySQL was not designed properly into a repository where it s!, but the data sharding must be done by DBAs and engineers data environments far... Is expected to grow to 1MM in the world, only trailing Oracle ’ s offering. Is especially true since most data environments go far beyond conventional relational database and data warehouse platforms to! Rewrite documentation here but we would still like to go over some the! The version MySQL 5.6 the SSD and how can MariaDB AX is and how can AX! It can be 100GB when you can mysql handle big data 200GB of memory to effectively handle big data a regular basis MySQL.